-----------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\Dr. Badas\Desktop\Badas Simas PSRM Replication\BadasSimasPSRMreplication.log
  log type:  text
 opened on:  14 Mar 2021, 10:02:15

. 
. ///: Study 1
> 
. ///: Load the data
> use Study1NORCSurvey.dta 

.    ///: Creating relvative average and average importance measures 
>  gen missA=0

.  replace missA=1 if Q7A==0
(532 real changes made)

.  gen missB=0

.  replace missB=2 if Q7B==0
(506 real changes made)

.  gen missC=0

.  replace missC=3 if Q7C==0
(528 real changes made)

.  gen missD=0

.  replace missD=4 if Q7D==0
(516 real changes made)

.  gen missE=0

.  replace missE=5 if Q7E==0
(527 real changes made)

.  gen missF=0

.  replace missF=6 if Q7F==0
(499 real changes made)

.  gen missG=0

.  replace missG=7 if Q7G==0
(516 real changes made)

.  gen missH=0

.  replace missH=8 if Q7H==0
(531 real changes made)

.  gen missI=0

.  replace missI=9 if Q7I==0
(519 real changes made)

.  gen missJ=0

.  replace missJ=10 if Q7J==0
(506 real changes made)

.  gen missK=0

.  replace missK=11 if Q7K==0
(509 real changes made)

.  gen missL=0

.  replace missL=12 if Q7L==0
(515 real changes made)

.  gen missM=0

.  replace missM=13 if Q7M==0
(498 real changes made)

.  gen missN=0

.  replace missN=14 if Q7N==0
(525 real changes made)

.  gen missO=0

.  replace missO=15 if Q7O==0
(500 real changes made)

.  gen missP=0

.  replace missP=16 if Q7P==0
(518 real changes made)

.  gen missQ=0

.  replace missQ=17 if SupremeCourt==.
(509 real changes made)

.  gen missR=0

.  replace missR=18 if Q7R==0
(508 real changes made)

.  
.  egen random=rowtotal(missA-missR)

. 
.  recode Q7A-Q7R (0=.), gen(a b c d e f g h i j k l m n o p q r)
(532 differences between Q7A and a)
(506 differences between Q7B and b)
(528 differences between Q7C and c)
(516 differences between Q7D and d)
(527 differences between Q7E and e)
(499 differences between Q7F and f)
(516 differences between Q7G and g)
(531 differences between Q7H and h)
(519 differences between Q7I and i)
(506 differences between Q7J and j)
(509 differences between Q7K and k)
(515 differences between Q7L and l)
(498 differences between Q7M and m)
(525 differences between Q7N and n)
(500 differences between Q7O and o)
(518 differences between Q7P and p)
(0 differences between SupremeCourt and q)
(508 differences between Q7R and r)

.  egen fullave=rowmean(a b c d e f g h i j k l m n o p r)
(1 missing value generated)

.  gen SCimport=SupremeCourt-fullave
(509 missing values generated)

.  
.   
.  
.  ///: Recode Party Variables 
>   gen indbase=.
(1,022 missing values generated)

.  replace indbase=1 if demo==1
(165 real changes made)

.  replace indbase=2 if demo==2
(219 real changes made)

.  replace indbase=3 if indep==1
(118 real changes made)

.  replace indbase=0 if indep==3
(137 real changes made)

.  replace indbase=4 if indep==2
(122 real changes made)

.  replace indbase=5 if repub==2
(153 real changes made)

.  replace indbase=6 if repub==1
(99 real changes made)

.  
.  recode indbase (0=4) (4=5) (5=6) (6=7), gen(pid7)
(511 differences between indbase and pid7)

.  recode pid7 (1 2 3=1) (4=0) (5 6 7=2), gen(pid3)
(848 differences between pid7 and pid3)

.  recode pid7 (1 7=3) (2 6=2) (3 5=1) (4=0), gen(pidx)
(794 differences between pid7 and pidx)

.  
.  ///: Generate White indicator variable 
>  gen white = 1 if raceth ==1
(383 missing values generated)

.  recode white . = 0 
(white: 383 changes made)

.  recode gender 1=0 2=1
(gender: 1022 changes made)

.  
.  ///: Figure 1. Distrubtion of Supreme Court Importance
>  histogram SCimport, xline(-.1513622)
(bin=22, start=-4, width=.27922078)
(note:  named style line not found in class linepattern, default attributes used)

.  graph save Figure1, replace
(file Figure1.gph saved)

.  
.  ///: Table 1. OLS Regression: Relative Importance of Supreme Court Appointments  
> reg SCimport fullave b4.pid7 gender white edu age hhincome SURV_MOD  [pweight = finalwt]
(sum of wgt is 518.6245210502)

Linear regression                               Number of obs     =        511
                                                F(13, 497)        =       6.48
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1693
                                                Root MSE          =     .93777

------------------------------------------------------------------------------
             |               Robust
    SCimport |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     fullave |  -.3065507   .0750989    -4.08   0.000    -.4541011   -.1590003
             |
        pid7 |
          1  |   .4879991   .2121511     2.30   0.022     .0711755    .9048226
          2  |   .2834711   .1801174     1.57   0.116    -.0704143    .6373565
          3  |   .4808544   .1943063     2.47   0.014     .0990914    .8626174
          5  |   .3836751   .2378468     1.61   0.107    -.0836341    .8509842
          6  |   .4213654   .1940199     2.17   0.030     .0401651    .8025658
          7  |   .9124072   .1812952     5.03   0.000     .5562077    1.268607
             |
      gender |  -.2400921   .1020353    -2.35   0.019    -.4405659   -.0396183
       white |  -.0830205   .1039573    -0.80   0.425    -.2872704    .1212294
    educatio |   .0827374   .0477166     1.73   0.084    -.0110138    .1764885
      agegrp |   .1078415   .0392252     2.75   0.006     .0307739    .1849091
    hhincome |   .0449955   .0236561     1.90   0.058    -.0014827    .0914737
    SURV_MOD |   .1004882   .1312181     0.77   0.444    -.1573223    .3582987
       _cons |  -.1896822   .4132787    -0.46   0.646    -1.001671    .6223066
------------------------------------------------------------------------------

. ///: Figure 2 
> margins pid7, plot 

Predictive margins                              Number of obs     =        511
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        pid7 |
          1  |  -.0817429   .1482032    -0.55   0.581     -.372925    .2094391
          2  |  -.2862709   .1110487    -2.58   0.010    -.5044537   -.0680881
          3  |  -.0888876   .1380373    -0.64   0.520    -.3600962    .1823209
          4  |   -.569742   .1450371    -3.93   0.000    -.8547035   -.2847805
          5  |   -.186067   .1857718    -1.00   0.317    -.5510619     .178928
          6  |  -.1483766   .1269359    -1.17   0.243    -.3977738    .1010206
          7  |   .3426651   .1060938     3.23   0.001     .1342175    .5511127
------------------------------------------------------------------------------

  Variables that uniquely identify margins: pid7

. graph save Figure2, replace
(file Figure2.gph saved)

. 
. 
. 
. ///: Online Appendix for Study 1
> 
. ///: Appendix Table 1. Summary stats. 
> sum SCimport fullave pid7 gender white edu age hhincome SURV_MOD if SCimport!=.

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    SCimport |        513   -.1513622    1.008099         -4   2.142857
     fullave |        513    3.837522    .6815581          1          5
        pid7 |        511    3.755382    2.011936          1          7
      gender |        513    .5087719     .500411          0          1
       white |        513    .6335283    .4823108          0          1
-------------+---------------------------------------------------------
    educatio |        513    2.957115    1.204692          1          5
      agegrp |        513    2.890838    1.359204          1          5
    hhincome |        513    5.126706    2.355497          1          9
    SURV_MOD |        513    1.810916    .3919574          1          2

. 
. ///: Appendix Table 2, Column 1
>  reg Lik SCimport fullave b7.pid7 gender white edu age hhincome SURV_MOD  [pweight = finalwt]
(sum of wgt is 518.6245210502)

Linear regression                               Number of obs     =        511
                                                F(14, 496)        =      13.49
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3455
                                                Root MSE          =     2.9536

------------------------------------------------------------------------------
             |               Robust
Likihood_v~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    SCimport |    .613958   .1758168     3.49   0.001     .2685204    .9593956
     fullave |   1.437988   .2992235     4.81   0.000     .8500859     2.02589
             |
        pid7 |
          1  |  -.0200226   .3865913    -0.05   0.959    -.7795811    .7395358
          2  |  -1.377458   .5096023    -2.70   0.007    -2.378703   -.3762129
          3  |  -2.323818   .7543304    -3.08   0.002    -3.805895   -.8417409
          4  |  -2.149961   .6497821    -3.31   0.001    -3.426626    -.873296
          5  |  -1.142209   .4683277    -2.44   0.015     -2.06236   -.2220582
          6  |  -.0524796   .3779289    -0.14   0.890    -.7950186    .6900594
             |
      gender |  -.0547427   .3236697    -0.17   0.866    -.6906754    .5811899
       white |   .0406956   .4198739     0.10   0.923     -.784255    .8656462
    educatio |   .5551297    .141689     3.92   0.000     .2767451    .8335142
      agegrp |   .3242269   .1249214     2.60   0.010     .0787865    .5696672
    hhincome |    .250293   .0746801     3.35   0.001     .1035646    .3970213
    SURV_MOD |  -.5231706    .485774    -1.08   0.282    -1.477599    .4312578
       _cons |   1.363277   1.453624     0.94   0.349    -1.492743    4.219296
------------------------------------------------------------------------------

. ///: Appendix Figure 1 
> margins, at(SCimport=(-4(.5)2)) plot

Predictive margins                              Number of obs     =        511
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : SCimport        =          -4

2._at        : SCimport        =        -3.5

3._at        : SCimport        =          -3

4._at        : SCimport        =        -2.5

5._at        : SCimport        =          -2

6._at        : SCimport        =        -1.5

7._at        : SCimport        =          -1

8._at        : SCimport        =         -.5

9._at        : SCimport        =           0

10._at       : SCimport        =          .5

11._at       : SCimport        =           1

12._at       : SCimport        =         1.5

13._at       : SCimport        =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   6.434762   .7103944     9.06   0.000     5.039008    7.830515
          2  |   6.741741     .62496    10.79   0.000     5.513846    7.969636
          3  |    7.04872   .5403198    13.05   0.000     5.987122    8.110318
          4  |   7.355699   .4569155    16.10   0.000      6.45797    8.253427
          5  |   7.662678   .3755714    20.40   0.000     6.924771    8.400585
          6  |   7.969657   .2979794    26.75   0.000     7.384199    8.555114
          7  |   8.276636    .228003    36.30   0.000     7.828665    8.724607
          8  |   8.583615    .175028    49.04   0.000     8.239727    8.927502
          9  |   8.890594   .1572895    56.52   0.000     8.581558     9.19963
         10  |   9.197573   .1852052    49.66   0.000     8.833689    9.561456
         11  |   9.504552   .2435523    39.02   0.000      9.02603    9.983073
         12  |   9.811531    .315896    31.06   0.000     9.190872    10.43219
         13  |   10.11851    .394612    25.64   0.000     9.343193    10.89383
------------------------------------------------------------------------------

  Variables that uniquely identify margins: SCimport

. graph save AppendixFigure1, replace 
(file AppendixFigure1.gph saved)

. ///: Appendix Table 2, Column 2 
> gen certain = 0

. replace certain = 1 if Lik ==11
(655 real changes made)

. 
. logit certain SCimport fullave b7.pid7 gender white edu age hhincome SURV_MOD  [pweight = finalwt]

Iteration 0:   log pseudolikelihood = -338.57306  
Iteration 1:   log pseudolikelihood =  -256.7308  
Iteration 2:   log pseudolikelihood = -253.51941  
Iteration 3:   log pseudolikelihood = -253.50002  
Iteration 4:   log pseudolikelihood = -253.50002  

Logistic regression                             Number of obs     =        511
                                                Wald chi2(14)     =      97.86
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -253.50002               Pseudo R2         =     0.2513

------------------------------------------------------------------------------
             |               Robust
     certain |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    SCimport |   .4428534   .1428142     3.10   0.002     .1629426    .7227641
     fullave |   .7570448   .2185653     3.46   0.001     .3286646    1.185425
             |
        pid7 |
          1  |  -.5937928     .60425    -0.98   0.326    -1.778101    .5905154
          2  |  -1.777651   .5454083    -3.26   0.001    -2.846632   -.7086707
          3  |  -2.179086   .6007264    -3.63   0.000    -3.356488   -1.001684
          4  |  -1.716536   .6238059    -2.75   0.006    -2.939173   -.4938986
          5  |  -1.550647   .5840088    -2.66   0.008    -2.695283    -.406011
          6  |    -.66715   .5836121    -1.14   0.253    -1.811009    .4767087
             |
      gender |   .0592882   .2582367     0.23   0.818    -.4468464    .5654228
       white |  -.0290257   .3159895    -0.09   0.927    -.6483536    .5903023
    educatio |   .4196616   .1135321     3.70   0.000     .1971428    .6421805
      agegrp |   .4038378   .1101248     3.67   0.000     .1879971    .6196785
    hhincome |   .1278352   .0601015     2.13   0.033     .0100383     .245632
    SURV_MOD |  -.2669675   .3974993    -0.67   0.502    -1.046052    .5121168
       _cons |  -3.421479    1.36353    -2.51   0.012    -6.093949   -.7490099
------------------------------------------------------------------------------

. ///: Appendix Figure 2 
> margins, at(SCimport=(-4(.5)2)) plot

Predictive margins                              Number of obs     =        511
Model VCE    : Robust

Expression   : Pr(certain), predict()

1._at        : SCimport        =          -4

2._at        : SCimport        =        -3.5

3._at        : SCimport        =          -3

4._at        : SCimport        =        -2.5

5._at        : SCimport        =          -2

6._at        : SCimport        =        -1.5

7._at        : SCimport        =          -1

8._at        : SCimport        =         -.5

9._at        : SCimport        =           0

10._at       : SCimport        =          .5

11._at       : SCimport        =           1

12._at       : SCimport        =         1.5

13._at       : SCimport        =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .347904   .0950724     3.66   0.000     .1615656    .5342424
          2  |   .3853833   .0863292     4.46   0.000     .2161813    .5545854
          3  |   .4241086   .0761363     5.57   0.000     .2748842    .5733331
          4  |   .4637092   .0648499     7.15   0.000     .3366058    .5908127
          5  |   .5037753   .0529652     9.51   0.000     .3999654    .6075852
          6  |   .5438724   .0411889    13.20   0.000     .4631437    .6246012
          7  |   .5835592    .030653    19.04   0.000     .5234805     .643638
          8  |   .6224059   .0234128    26.58   0.000     .5765177    .6682941
          9  |   .6600115   .0221465    29.80   0.000     .6166051    .7034179
         10  |   .6960186   .0264347    26.33   0.000     .6442076    .7478296
         11  |   .7301236   .0329177    22.18   0.000      .665606    .7946412
         12  |    .762083   .0393599    19.36   0.000     .6849391    .8392269
         13  |   .7917169   .0448118    17.67   0.000     .7038873    .8795464
------------------------------------------------------------------------------

  Variables that uniquely identify margins: SCimport

.  graph save AppendixFigure2, replace
(file AppendixFigure2.gph saved)

. 
. clear

. 
. ///: Study 2 
> ///: Load the data
> use study2CCES.dta 

. 
. ///: Generate congruence variables 
> gen votedem=.
(60,000 missing values generated)

. replace votedem=1 if SenCand1Party=="Democratic" & CC18_410b==1
(15,660 real changes made)

. replace votedem=0 if SenCand2Party=="Republican" & CC18_410b==2
(10,277 real changes made)

. 
. gen deminc=.
(60,000 missing values generated)

. replace deminc=1 if inputstate==9 | inputstate==10 | inputstate==12
(5,287 real changes made)

. replace deminc=1 if inputstate==15 | inputstate==18 | inputstate==24
(2,638 real changes made)

. replace deminc=1 if inputstate==25 | inputstate==26 | inputstate==27
(4,087 real changes made)

. replace deminc=1 if inputstate==29 | inputstate==30 | inputstate==34
(3,305 real changes made)

. replace deminc=1 if inputstate==35 | inputstate==36 | inputstate==38
(4,011 real changes made)

. replace deminc=1 if inputstate==39 | inputstate==42 | inputstate==44
(5,881 real changes made)

. replace deminc=1 if inputstate==51 | inputstate==53 | inputstate==54 | inputstate==55
(4,806 real changes made)

. replace deminc=0 if inputstate==28 | inputstate==31 | inputstate==32
(1,432 real changes made)

. replace deminc=0 if inputstate==48 | inputstate==56
(4,545 real changes made)

. 
. gen voteinc=1 if votedem==1 & deminc==1
(48,615 missing values generated)

. replace voteinc=1 if votedem==0 & deminc==0
(1,102 real changes made)

. replace voteinc=0 if votedem==1 & deminc==0
(1,292 real changes made)

. replace voteinc=0 if votedem==0 & deminc==1
(7,869 real changes made)

. 
. gen ng_y=.
(60,000 missing values generated)

. replace ng_y=1 if deminc==0
(5,977 real changes made)

. replace ng_y=0 if deminc==1
(30,015 real changes made)

. replace ng_y=1 if inputstate==18 | inputstate==38 | inputstate==54
(2,018 real changes made)

. 
. gen bk_y=.
(60,000 missing values generated)

. replace bk_y=1 if deminc==0
(5,977 real changes made)

. replace bk_y=0 if deminc==1
(30,015 real changes made)

. 
. gen ng_same=.
(60,000 missing values generated)

. replace ng_same=1 if ng_y==1 & Gorsuch_support==1
(4,089 real changes made)

. replace ng_same=1 if ng_y==0 & Gorsuch_support==0
(14,675 real changes made)

. replace ng_same=0 if ng_y==1 & Gorsuch_support==0
(3,883 real changes made)

. replace ng_same=0 if ng_y==0 & Gorsuch_support==1
(13,257 real changes made)

. 
. gen bk_same=.
(60,000 missing values generated)

. replace bk_same=1 if bk_y==1 & Kavanaugh_support==1
(2,776 real changes made)

. replace bk_same=1 if bk_y==0 & Kavanaugh_support==0
(17,322 real changes made)

. replace bk_same=0 if bk_y==1 & Kavanaugh_support==0
(3,190 real changes made)

. replace bk_same=0 if bk_y==0 & Kavanaugh_support==1
(12,646 real changes made)

. 
. egen courttot=rowtotal(ng_same bk_same)

. 
. 
. recode CC18_321c (1=1) (2=0), gen(resp_abort_y)
(21634 differences between CC18_321c and resp_abort_y)

. gen inc_abort_y=1 if deminc==0
(54,023 missing values generated)

. replace inc_abort_y=0 if deminc==1
(30,015 real changes made)

. replace inc_abort_y=1 if inputstate==42 | inputstate==54
(3,597 real changes made)

. gen abort_same=1 if resp_abort_y==1 & inc_abort_y==1
(53,561 missing values generated)

. replace abort_same=1 if resp_abort_y==0 & inc_abort_y==0
(9,795 real changes made)

. replace abort_same=0 if resp_abort_y==1 & inc_abort_y==0
(16,588 real changes made)

. replace abort_same=0 if resp_abort_y==0 & inc_abort_y==1
(3,118 real changes made)

. 
. recode pid7 (8=.), gen(pidr)
(2050 differences between pid7 and pidr)

. gen pidd=abs(pidr-8)
(2,141 missing values generated)

. gen pidinc=pidr if deminc==0
(54,275 missing values generated)

. replace pidinc=pidd if deminc==1
(28,915 real changes made)

. recode pid7 8 = . 
(pid7: 2050 changes made)

. 
. 
. recode CC18_334A CC18_334I CC18_334J (8=.), gen(lcself lcsen1 lcsen2)
(3329 differences between CC18_334A and lcself)
(8701 differences between CC18_334I and lcsen1)
(15986 differences between CC18_334J and lcsen2)

. 
. gen incdist=abs(lcself-lcsen1) if deminc==1
(36,176 missing values generated)

. replace incdist=abs(lcself-lcsen2) if deminc==0
(5,066 real changes made)

. 
. gen incdist01=incdist/6
(31,110 missing values generated)

. gen pidinc01=(pidinc-1)/6
(25,360 missing values generated)

. 
. 
. ///: Generate Knowledge
> recode CC18_309a (1=1) (else=0), gen(k1)
(15756 differences between CC18_309a and k1)

. recode CC18_309b (1=1) (else=0), gen(k2)
(15969 differences between CC18_309b and k2)

. gen k3=0

. replace k3=1 if CC18_310a==2 & CurrentGovParty=="Republican"
(27,962 real changes made)

. replace k3=1 if CC18_310a==3 & CurrentGovParty=="Democratic"
(17,971 real changes made)

. replace k3=1 if CC18_310a==4 & CurrentGovParty=="Independent"
(63 real changes made)

. gen k4=0

. replace k4=1 if CC18_310b==2 & CurrentSen1Party=="Republican"
(15,344 real changes made)

. replace k4=1 if CC18_310b==3 & CurrentSen1Party=="Democratic"
(27,668 real changes made)

. replace k4=1 if CC18_310b==4 & CurrentSen1Party=="Independent"
(323 real changes made)

. gen k5=0

. replace k5=1 if CC18_310c==2 & CurrentSen2Party=="Republican"
(22,218 real changes made)

. replace k5=1 if CC18_310c==3 & CurrentSen2Party=="Democratic"
(21,055 real changes made)

. replace k5=1 if CC18_310c==4 & CurrentSen2Party=="Independent"
(0 real changes made)

. gen k6=0

. replace k6=1 if CC18_310d==2 & CurrentHouseParty=="Republican"
(22,191 real changes made)

. replace k6=1 if CC18_310d==3 & CurrentHouseParty=="Democratic"
(16,565 real changes made)

. replace k6=1 if CC18_310d==4 & CurrentHouseParty=="Independent"
(0 real changes made)

. egen know=rowmean(k1 k2 k3 k4 k5 k6)

. 
. 
. 
. ///: Create indicator for verified voter 
> gen verified=0

. replace verified=1 if CL_2018gvm!=.
(33,762 real changes made)

. 
. ///: Recode income missing
> recode faminc_new (97=.), gen(income)
(5892 differences between faminc_new and income)

. 
. ///: Figure 3
>    ///: Remove independents 
> gen repdem = . 
(60,000 missing values generated)

. replace repdem = 1 if pid7 >4
(23,861 real changes made)

. replace repdem =2 if pid7 <4
(27,607 real changes made)

. 
. histogram courttot, discrete width(1) percent by(deminc repdem, rows(1))

. graph save Figure3, replace
(note: file Figure3.gph not found)
(file Figure3.gph saved)

. ///: Table 2: Logit models predicting incumbent vote 
> 
. ///: Table 2 Column 1: Logit models for everyone
> logit voteinc (i.courttot abort_same c.pidinc c.incdist)##c.know gender white educ age /// 
> income i.inputstate if verified==1

Iteration 0:   log likelihood = -9006.0245  
Iteration 1:   log likelihood = -1927.9741  
Iteration 2:   log likelihood = -1736.4293  
Iteration 3:   log likelihood =   -1718.26  
Iteration 4:   log likelihood = -1718.0469  
Iteration 5:   log likelihood = -1718.0467  

Logistic regression                             Number of obs     =     13,215
                                                LR chi2(42)       =   14575.96
                                                Prob > chi2       =     0.0000
Log likelihood = -1718.0467                     Pseudo R2         =     0.8092

-----------------------------------------------------------------------------------
          voteinc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         courttot |
               1  |   .1007605   .3697313     0.27   0.785    -.6238995    .8254205
               2  |   .6947309   .4278629     1.62   0.104     -.143865    1.533327
                  |
     1.abort_same |   .3062084   .3210801     0.95   0.340    -.3230969    .9355138
           pidinc |   .7936288   .0878266     9.04   0.000     .6214918    .9657659
          incdist |  -.2676471   .1036182    -2.58   0.010    -.4707352   -.0645591
             know |   .0421365   .6116542     0.07   0.945    -1.156684    1.240957
                  |
  courttot#c.know |
               1  |   2.145714   .4408933     4.87   0.000     1.281579    3.009849
               2  |   3.469209    .513449     6.76   0.000     2.462868    4.475551
                  |
abort_same#c.know |
               1  |    .422213   .3765518     1.12   0.262     -.315815    1.160241
                  |
  c.pidinc#c.know |   .0544381     .10699     0.51   0.611    -.1552583    .2641346
                  |
 c.incdist#c.know |   -.662611   .1258416    -5.27   0.000     -.909256    -.415966
                  |
           gender |  -.0824813   .0961753    -0.86   0.391    -.2709814    .1060188
            white |  -.4697852    .131658    -3.57   0.000    -.7278301   -.2117404
             educ |   .0327443   .0343622     0.95   0.341    -.0346045     .100093
              age |   .0065618   .0030519     2.15   0.032     .0005802    .0125435
           income |  -.0114312   .0156507    -0.73   0.465    -.0421059    .0192435
                  |
       inputstate |
        Delaware  |  -.2440218   .7468432    -0.33   0.744    -1.707808    1.219764
         Florida  |   -.354245   .3536708    -1.00   0.317    -1.047427     .338937
          Hawaii  |  -1.123474    .678279    -1.66   0.098    -2.452876    .2059287
         Indiana  |    .051108   .3868954     0.13   0.895    -.7071932    .8094091
        Maryland  |   .3080742   .4497195     0.69   0.493    -.5733599    1.189508
   Massachusetts  |  -.0984222   .4059179    -0.24   0.808    -.8940066    .6971622
        Michigan  |  -.3984321   .3795236    -1.05   0.294    -1.142285    .3454206
       Minnesota  |   .7253174   .4203276     1.73   0.084    -.0985096    1.549144
     Mississippi  |  -.5383707    .544103    -0.99   0.322    -1.604793    .5280517
        Missouri  |   .2654952   .3933126     0.68   0.500    -.5053834    1.036374
         Montana  |   .4742319   .5750341     0.82   0.410    -.6528141    1.601278
        Nebraska  |   -2.00184   .5157467    -3.88   0.000    -3.012685   -.9909952
          Nevada  |  -.9645202   .4698304    -2.05   0.040    -1.885371   -.0436695
      New Jersey  |  -.4759879    .402696    -1.18   0.237    -1.265257    .3132818
      New Mexico  |  -.7610047   .5528583    -1.38   0.169    -1.844587    .3225777
        New York  |   .5545629    .369944     1.50   0.134     -.170514     1.27964
    North Dakota  |  -.5319131   .5421547    -0.98   0.327    -1.594517    .5306906
            Ohio  |   .6955978   .3682694     1.89   0.059    -.0261969    1.417393
    Pennsylvania  |   .5884266   .3858504     1.53   0.127    -.1678263    1.344679
    Rhode Island  |   1.055619    .661757     1.60   0.111     -.241401    2.352639
           Texas  |  -1.902781   .3780568    -5.03   0.000    -2.643759   -1.161803
        Virginia  |   .3822293    .397452     0.96   0.336    -.3967622    1.161221
      Washington  |  -.3099628   .4073014    -0.76   0.447    -1.108259    .4883331
   West Virginia  |  -.6542622   .4263162    -1.53   0.125    -1.489827    .1813023
       Wisconsin  |   .8577785   .4173045     2.06   0.040     .0398768     1.67568
         Wyoming  |  -3.689162   .6919599    -5.33   0.000    -5.045379   -2.332946
                  |
            _cons |   -2.84137   .6688538    -4.25   0.000      -4.1523   -1.530441
-----------------------------------------------------------------------------------

. 
. ///: Figure 4 
> margins courttot, at(know=(.0 .1 to 1)) plot

Predictive margins                              Number of obs     =     13,215
Model VCE    : OIM

Expression   : Pr(voteinc), predict()

1._at        : know            =           0

2._at        : know            =          .1

3._at        : know            =          .2

4._at        : know            =          .3

5._at        : know            =          .4

6._at        : know            =          .5

7._at        : know            =          .6

8._at        : know            =          .7

9._at        : know            =          .8

10._at       : know            =          .9

11._at       : know            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at#courttot |
        1 0  |   .5078764   .0341603    14.87   0.000     .4409235    .5748293
        1 1  |   .5186973   .0258513    20.06   0.000     .4680297     .569365
        1 2  |   .5812528   .0340639    17.06   0.000     .5144887    .6480168
        2 0  |   .4986527   .0291219    17.12   0.000     .4415747    .5557307
        2 1  |   .5307395   .0214576    24.73   0.000     .4886834    .5727955
        2 2  |   .6024826   .0289273    20.83   0.000     .5457862    .6591791
        3 0  |    .490553   .0245804    19.96   0.000     .4423762    .5387298
        3 1  |    .541694   .0175883    30.80   0.000     .5072216    .5761664
        3 2  |    .621359   .0244202    25.44   0.000     .5734964    .6692217
        4 0  |   .4834021    .020477    23.61   0.000     .4432679    .5235363
        4 1  |   .5516368   .0142057    38.83   0.000      .523794    .5794795
        4 2  |   .6381794   .0204223    31.25   0.000     .5981525    .6782063
        5 0  |   .4770437    .016786    28.42   0.000     .4441437    .5099436
        5 1  |   .5606485   .0112765    49.72   0.000      .538547      .58275
        5 2  |   .6532176   .0168631    38.74   0.000     .6201665    .6862687
        6 0  |   .4713438    .013537    34.82   0.000     .4448117    .4978759
        6 1  |   .5688109   .0087839    64.76   0.000     .5515948    .5860269
        6 2  |   .6667163   .0137408    48.52   0.000     .6397848    .6936478
        7 0  |   .4661909    .010853    42.95   0.000     .4449194    .4874625
        7 1  |   .5762038   .0067479    85.39   0.000     .5629782    .5894294
        7 2  |   .6788852   .0111488    60.89   0.000      .657034    .7007364
        8 0  |   .4614938   .0090015    51.27   0.000     .4438512    .4791363
        8 1  |   .5829034   .0052558   110.91   0.000     .5726022    .5932045
        8 2  |   .6899026   .0093098    74.11   0.000     .6716558    .7081494
        9 0  |   .4571781   .0083473    54.77   0.000     .4408178    .4735385
        9 1  |   .5889808   .0044697   131.77   0.000     .5802202    .5977413
        9 2  |   .6999192   .0085367    81.99   0.000     .6831877    .7166508
       10 0  |   .4531841   .0090083    50.31   0.000     .4355281    .4708402
       10 1  |   .5945016   .0044795   132.72   0.000      .585722    .6032812
       10 2  |   .7090632   .0089679    79.07   0.000     .6914863      .72664
       11 0  |   .4494633    .010645    42.22   0.000     .4285995    .4703272
       11 1  |   .5995253   .0051007   117.54   0.000      .589528    .6095226
       11 2  |   .7174437    .010352    69.30   0.000     .6971541    .7377333
------------------------------------------------------------------------------

  Variables that uniquely identify margins: know courttot

. graph save Figure4, replace
(note: file Figure4.gph not found)
(file Figure4.gph saved)

. 
. ///: Figure 5 
> margins, dydx(courttot abort_same) at(know=(.0 .1 to 1)) plot(legend(ring(0)))

Average marginal effects                        Number of obs     =     13,215
Model VCE    : OIM

Expression   : Pr(voteinc), predict()
dy/dx w.r.t. : 1.courttot 2.courttot 1.abort_same

1._at        : know            =           0

2._at        : know            =          .1

3._at        : know            =          .2

4._at        : know            =          .3

5._at        : know            =          .4

6._at        : know            =          .5

7._at        : know            =          .6

8._at        : know            =          .7

9._at        : know            =          .8

10._at       : know            =          .9

11._at       : know            =           1

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
0.courttot    |  (base outcome)
--------------+----------------------------------------------------------------
1.courttot    |
          _at |
           1  |   .0108209   .0397848     0.27   0.786    -.0671557    .0887976
           2  |   .0320868   .0337001     0.95   0.341    -.0339643    .0981378
           3  |    .051141   .0283091     1.81   0.071    -.0043438    .1066258
           4  |   .0682347   .0235092     2.90   0.004     .0221575    .1143118
           5  |   .0836048   .0192412     4.35   0.000     .0458927    .1213169
           6  |   .0974671    .015516     6.28   0.000     .0670563    .1278779
           7  |   .1100128   .0124572     8.83   0.000     .0855972    .1344285
           8  |   .1214096   .0103577    11.72   0.000     .1011089    .1417103
           9  |   .1318027   .0096219    13.70   0.000     .1129441    .1506612
          10  |   .1413174   .0103787    13.62   0.000     .1209756    .1616592
          11  |    .150062   .0122466    12.25   0.000      .126059    .1740649
--------------+----------------------------------------------------------------
2.courttot    |
          _at |
           1  |   .0733764   .0466801     1.57   0.116    -.0181149    .1648677
           2  |     .10383   .0402195     2.58   0.010     .0250011    .1826588
           3  |    .130806   .0344153     3.80   0.000     .0633533    .1982587
           4  |   .1547773   .0291212     5.31   0.000     .0977008    .2118537
           5  |   .1761739    .024286     7.25   0.000     .1285742    .2237737
           6  |   .1953725   .0199766     9.78   0.000     .1562192    .2345259
           7  |   .2126943   .0164205    12.95   0.000     .1805107    .2448779
           8  |   .2284088   .0140539    16.25   0.000     .2008637     .255954
           9  |   .2427411   .0133979    18.12   0.000     .2164816    .2690006
          10  |    .255879     .01458    17.55   0.000     .2273027    .2844554
          11  |   .2679804   .0171353    15.64   0.000     .2343958     .301565
--------------+----------------------------------------------------------------
0.abort_same  |  (base outcome)
--------------+----------------------------------------------------------------
1.abort_same  |
          _at |
           1  |   .0298283   .0316376     0.94   0.346    -.0321802    .0918367
           2  |   .0302015   .0251225     1.20   0.229    -.0190377    .0794407
           3  |   .0301357   .0196982     1.53   0.126     -.008472    .0687435
           4  |   .0297739   .0152454     1.95   0.051    -.0001065    .0596542
           5  |   .0292302   .0116474     2.51   0.012     .0064016    .0520587
           6  |   .0285915   .0088092     3.25   0.001     .0113257    .0458573
           7  |   .0279204   .0066737     4.18   0.000     .0148402    .0410005
           8  |   .0272589   .0052319     5.21   0.000     .0170047    .0375132
           9  |   .0266334   .0044929     5.93   0.000     .0178274    .0354394
          10  |   .0260583   .0043799     5.95   0.000     .0174738    .0346428
          11  |   .0255405   .0046859     5.45   0.000     .0163564    .0347246
-------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

  Variables that uniquely identify margins: know _deriv

. graph save Figure5, replace
(note: file Figure5.gph not found)
(file Figure5.gph saved)

. 
. 
. ///: Table 2 Column 2: Logit models for Republicans 
> logit voteinc (i.courttot abort_same c.pidinc c.incdist)##c.know gender white educ age ///
> income i.inputstate if verified==1& pid7>4

note: 15.inputstate != 0 predicts failure perfectly
      15.inputstate dropped and 27 obs not used

note: 28.inputstate != 0 predicts success perfectly
      28.inputstate dropped and 79 obs not used

note: 35.inputstate != 0 predicts failure perfectly
      35.inputstate dropped and 63 obs not used

Iteration 0:   log likelihood = -2322.2796  
Iteration 1:   log likelihood = -957.59438  
Iteration 2:   log likelihood =  -816.0719  
Iteration 3:   log likelihood = -774.11244  
Iteration 4:   log likelihood = -773.66363  
Iteration 5:   log likelihood = -773.66269  
Iteration 6:   log likelihood = -773.66269  

Logistic regression                             Number of obs     =      5,019
                                                LR chi2(39)       =    3097.23
                                                Prob > chi2       =     0.0000
Log likelihood = -773.66269                     Pseudo R2         =     0.6669

-----------------------------------------------------------------------------------
          voteinc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         courttot |
               1  |  -.0310614    .535531    -0.06   0.954    -1.080683     1.01856
               2  |   .3796558   .7363671     0.52   0.606    -1.063597    1.822909
                  |
     1.abort_same |   .6684234   .5171019     1.29   0.196    -.3450777    1.681925
           pidinc |   .9962321   .2074785     4.80   0.000     .5895816    1.402883
          incdist |  -.0619891   .1681888    -0.37   0.712    -.3916331    .2676548
             know |   3.099982   .8904474     3.48   0.000     1.354737    4.845227
                  |
  courttot#c.know |
               1  |   1.678116   .6431881     2.61   0.009     .4174901    2.938741
               2  |    3.44696   .8897007     3.87   0.000     1.703178    5.190741
                  |
abort_same#c.know |
               1  |  -.2177091    .616236    -0.35   0.724     -1.42551    .9900913
                  |
  c.pidinc#c.know |  -.6365926   .2255656    -2.82   0.005    -1.078693   -.1944921
                  |
 c.incdist#c.know |   -1.03871   .2061056    -5.04   0.000    -1.442669     -.63475
                  |
           gender |  -.0721303   .1455753    -0.50   0.620    -.3574527    .2131922
            white |  -.8480999   .2213746    -3.83   0.000    -1.281986   -.4142138
             educ |   .0151799   .0520128     0.29   0.770    -.0867633    .1171231
              age |   .0133303   .0046416     2.87   0.004     .0042329    .0224276
           income |   .0232556   .0240192     0.97   0.333    -.0238211    .0703322
                  |
       inputstate |
        Delaware  |   .7356301   .9324748     0.79   0.430    -1.091987    2.563247
         Florida  |  -.8673801   .5506396    -1.58   0.115    -1.946614    .2118536
          Hawaii  |          0  (empty)
         Indiana  |  -.4513818   .5879876    -0.77   0.443    -1.603816    .7010527
        Maryland  |  -.0366845   .6594198    -0.06   0.956    -1.329123    1.255755
   Massachusetts  |  -.8381866   .7162442    -1.17   0.242    -2.241999    .5656262
        Michigan  |  -.6170227   .5973922    -1.03   0.302     -1.78789    .5538445
       Minnesota  |   .4011065   .6144621     0.65   0.514    -.8032171     1.60543
     Mississippi  |          0  (empty)
        Missouri  |  -.8555935   .6323188    -1.35   0.176    -2.094916    .3837285
         Montana  |   .3243865   .8481052     0.38   0.702    -1.337869    1.986642
        Nebraska  |  -.4844214   .8893301    -0.54   0.586    -2.227476    1.258634
          Nevada  |    .335339   .8749733     0.38   0.702    -1.379577    2.050255
      New Jersey  |   -1.44212   .6859813    -2.10   0.036    -2.786619   -.0976216
      New Mexico  |          0  (empty)
        New York  |   .5119602   .5546511     0.92   0.356    -.5751359    1.599056
    North Dakota  |  -1.037576   .8386743    -1.24   0.216    -2.681348    .6061952
            Ohio  |   .2203461   .5580342     0.39   0.693    -.8733808    1.314073
    Pennsylvania  |    .278159   .5727833     0.49   0.627    -.8444756    1.400794
    Rhode Island  |   .4836536    1.04937     0.46   0.645    -1.573074    2.540382
           Texas  |  -1.040385   .6782137    -1.53   0.125    -2.369659      .28889
        Virginia  |  -.2339268   .5992473    -0.39   0.696     -1.40843    .9405764
      Washington  |  -1.046314   .6709158    -1.56   0.119    -2.361285     .268657
   West Virginia  |  -1.280362   .6811964    -1.88   0.060    -2.615482    .0547587
       Wisconsin  |   .5035027   .5997081     0.84   0.401    -.6719035    1.678909
         Wyoming  |  -2.194683   .9803809    -2.24   0.025    -4.116194   -.2731718
                  |
            _cons |   -3.67113    1.00974    -3.64   0.000    -5.650185   -1.692075
-----------------------------------------------------------------------------------

. 
. ///: Figure 6, left pane
> margins courttot, at(know=(.0 .1 to 1)) plot

Predictive margins                              Number of obs     =      5,019
Model VCE    : OIM

Expression   : Pr(voteinc), predict()

1._at        : know            =           0

2._at        : know            =          .1

3._at        : know            =          .2

4._at        : know            =          .3

5._at        : know            =          .4

6._at        : know            =          .5

7._at        : know            =          .6

8._at        : know            =          .7

9._at        : know            =          .8

10._at       : know            =          .9

11._at       : know            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at#courttot |
        1 0  |   .2189656   .0496544     4.41   0.000     .1216448    .3162865
        1 1  |   .2158784   .0479431     4.50   0.000     .1219118    .3098451
        1 2  |    .260178   .0846072     3.08   0.002      .094351    .4260051
        2 0  |   .1969846   .0381123     5.17   0.000     .1222858    .2716833
        2 1  |   .2096096   .0398711     5.26   0.000     .1314637    .2877555
        2 2  |   .2732279   .0774305     3.53   0.000     .1214668     .424989
        3 0  |   .1786952   .0288248     6.20   0.000     .1221997    .2351906
        3 1  |   .2048352   .0326826     6.27   0.000     .1407784    .2688919
        3 2  |   .2878452   .0696212     4.13   0.000     .1513902    .4243002
        4 0  |   .1636556   .0216659     7.55   0.000     .1211911      .20612
        4 1  |   .2015043    .026374     7.64   0.000     .1498122    .2531963
        4 2  |    .303792   .0611885     4.96   0.000     .1838648    .4237192
        5 0  |    .151315   .0162929     9.29   0.000     .1193815    .1832484
        5 1  |   .1995121   .0209051     9.54   0.000      .158539    .2404853
        5 2  |   .3206743   .0523166     6.13   0.000     .2181357    .4232128
        6 0  |   .1411216   .0123084    11.47   0.000     .1169975    .1652456
        6 1  |   .1987093   .0162423    12.23   0.000     .1668749    .2305437
        6 2  |   .3380027   .0434233     7.78   0.000     .2528947    .4231108
        7 0  |   .1325929   .0094183    14.08   0.000     .1141335    .1510524
        7 1  |    .198914   .0124241    16.01   0.000     .1745632    .2232648
        7 2  |    .355282   .0352482    10.08   0.000     .2861968    .4243672
        8 0  |   .1253435   .0075532    16.59   0.000     .1105395    .1401474
        8 1  |   .1999278   .0096533    20.71   0.000     .1810077    .2188479
        8 2  |   .3720963   .0290177    12.82   0.000     .3152226    .4289701
        9 0  |   .1190791   .0068711    17.33   0.000      .105612    .1325463
        9 1  |   .2015514    .008328    24.20   0.000     .1852287     .217874
        9 2  |   .3881648   .0264086    14.70   0.000     .3364048    .4399247
       10 0  |   .1135805   .0074362    15.27   0.000     .0990059    .1281552
       10 1  |   .2035987   .0086535    23.53   0.000      .186638    .2205593
       10 2  |   .4033468   .0283622    14.22   0.000     .3477579    .4589357
       11 0  |   .1086865   .0089305    12.17   0.000      .091183      .12619
       11 1  |   .2059072   .0101797    20.23   0.000     .1859555     .225859
       11 2  |   .4176096   .0338862    12.32   0.000      .351194    .4840252
------------------------------------------------------------------------------

  Variables that uniquely identify margins: know courttot

. graph save Figure6left, replace
(file Figure6left.gph saved)

. 
. ///: Figure 7, left pane 
> margins, dydx(courttot abort_same) at(know=(.0 .1 to 1)) plot

Average marginal effects                        Number of obs     =      5,019
Model VCE    : OIM

Expression   : Pr(voteinc), predict()
dy/dx w.r.t. : 1.courttot 2.courttot 1.abort_same

1._at        : know            =           0

2._at        : know            =          .1

3._at        : know            =          .2

4._at        : know            =          .3

5._at        : know            =          .4

6._at        : know            =          .5

7._at        : know            =          .6

8._at        : know            =          .7

9._at        : know            =          .8

10._at       : know            =          .9

11._at       : know            =           1

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
0.courttot    |  (base outcome)
--------------+----------------------------------------------------------------
1.courttot    |
          _at |
           1  |  -.0030872   .0532361    -0.06   0.954    -.1074281    .1012537
           2  |    .012625   .0438665     0.29   0.773    -.0733517    .0986018
           3  |     .02614   .0358806     0.73   0.466    -.0441847    .0964647
           4  |   .0378487   .0291169     1.30   0.194    -.0192193    .0949167
           5  |   .0481972   .0233759     2.06   0.039     .0023811    .0940132
           6  |   .0575877   .0185297     3.11   0.002     .0212701    .0939053
           7  |    .066321   .0146065     4.54   0.000     .0376929    .0949492
           8  |   .0745843   .0118728     6.28   0.000      .051314    .0978546
           9  |   .0824722   .0107931     7.64   0.000     .0613181    .1036264
          10  |   .0900182   .0115461     7.80   0.000     .0673882    .1126481
          11  |   .0972207   .0136464     7.12   0.000     .0704744    .1239671
--------------+----------------------------------------------------------------
2.courttot    |
          _at |
           1  |   .0412124   .0839517     0.49   0.623    -.1233299    .2057547
           2  |   .0762433   .0762472     1.00   0.317    -.0731984    .2256851
           3  |     .10915   .0686228     1.59   0.112    -.0253482    .2436482
           4  |   .1401365   .0606813     2.31   0.021     .0212033    .2590696
           5  |   .1693593   .0523418     3.24   0.001     .0667712    .2719474
           6  |   .1968812   .0439186     4.48   0.000     .1108023    .2829601
           7  |    .222689   .0361865     6.15   0.000     .1517648    .2936132
           8  |   .2467529   .0305125     8.09   0.000     .1869494    .3065564
           9  |   .2690856   .0286862     9.38   0.000     .2128617    .3253095
          10  |   .2897663   .0315442     9.19   0.000     .2279407    .3515918
          11  |   .3089231   .0379854     8.13   0.000     .2344731    .3833731
--------------+----------------------------------------------------------------
0.abort_same  |  (base outcome)
--------------+----------------------------------------------------------------
1.abort_same  |
          _at |
           1  |   .0746819    .064049     1.17   0.244    -.0508518    .2002157
           2  |   .0652682   .0516102     1.26   0.206    -.0358859    .1664223
           3  |   .0566103   .0406176     1.39   0.163    -.0229988    .1362193
           4  |   .0489685   .0313378     1.56   0.118    -.0124525    .1103895
           5  |   .0424154   .0238232     1.78   0.075    -.0042772    .0891079
           6  |   .0368878   .0179852     2.05   0.040     .0016375    .0721381
           7  |   .0322554   .0137107     2.35   0.019     .0053829    .0591279
           8  |   .0283689   .0109406     2.59   0.010     .0069257     .049812
           9  |   .0250863   .0096217     2.61   0.009      .006228    .0439445
          10  |   .0222857   .0095259     2.34   0.019     .0036152    .0409562
          11  |   .0198689   .0102248     1.94   0.052    -.0001714    .0399092
-------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

  Variables that uniquely identify margins: know _deriv

. graph save Figure7left, replace
(file Figure7left.gph saved)

. 
. ///: Table 2 Column 3: Logit models for Democrats 
> logit voteinc (i.courttot i.abort_same c.pidinc c.incdist)##c.know gender white educ age ///
> income i.inputstate if verified==1& pid7<4

note: 44.inputstate != 0 predicts success perfectly
      44.inputstate dropped and 40 obs not used

note: 56.inputstate != 0 predicts failure perfectly
      56.inputstate dropped and 17 obs not used

Iteration 0:   log likelihood = -2458.8743  
Iteration 1:   log likelihood = -638.25434  
Iteration 2:   log likelihood = -582.00305  
Iteration 3:   log likelihood = -459.31408  
Iteration 4:   log likelihood = -449.00642  
Iteration 5:   log likelihood = -448.65084  
Iteration 6:   log likelihood = -448.64949  
Iteration 7:   log likelihood = -448.64949  

Logistic regression                             Number of obs     =      6,799
                                                LR chi2(40)       =    4020.45
                                                Prob > chi2       =     0.0000
Log likelihood = -448.64949                     Pseudo R2         =     0.8175

-----------------------------------------------------------------------------------
          voteinc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         courttot |
               1  |    .148974   .6825357     0.22   0.827    -1.188771    1.486719
               2  |    .436886   .7355822     0.59   0.553    -1.004829    1.878601
                  |
     1.abort_same |  -.2983506   .5670176    -0.53   0.599    -1.409685    .8129835
           pidinc |   .1584773   .2427345     0.65   0.514    -.3172737    .6342282
          incdist |  -.1899683   .1841322    -1.03   0.302    -.5508608    .1709242
             know |  -4.009965   1.905862    -2.10   0.035    -7.745386   -.2745445
                  |
  courttot#c.know |
               1  |   3.345098   .8650116     3.87   0.000     1.649706     5.04049
               2  |   4.745358   .9393328     5.05   0.000       2.9043    6.586417
                  |
abort_same#c.know |
               1  |   1.289646   .7086906     1.82   0.069    -.0993621    2.678654
                  |
  c.pidinc#c.know |   .5223382   .2756067     1.90   0.058     -.017841    1.062517
                  |
 c.incdist#c.know |   -.474135   .2378514    -1.99   0.046     -.940315   -.0079549
                  |
           gender |  -.4282762   .2102123    -2.04   0.042    -.8402846   -.0162677
            white |  -.8628844   .2499511    -3.45   0.001     -1.35278   -.3729892
             educ |    .068715   .0717942     0.96   0.339    -.0719991     .209429
              age |   .0082856   .0064046     1.29   0.196    -.0042673    .0208384
           income |  -.0418897   .0328009    -1.28   0.202    -.1061783    .0223988
                  |
       inputstate |
        Delaware  |  -1.103475   .9798734    -1.13   0.260    -3.023992    .8170418
         Florida  |  -.4673619   .5708348    -0.82   0.413    -1.586177    .6514537
          Hawaii  |  -2.046128   .9629815    -2.12   0.034    -3.933537   -.1587192
         Indiana  |   .7694512   .8156973     0.94   0.346    -.8292861    2.368189
        Maryland  |   .7054892   .8465155     0.83   0.405    -.9536508    2.364629
   Massachusetts  |  -.0215143   .6852796    -0.03   0.975    -1.364638    1.321609
        Michigan  |  -.2036536   .6319853    -0.32   0.747    -1.442322    1.035015
       Minnesota  |   1.270998   .8417035     1.51   0.131    -.3787102    2.920707
     Mississippi  |  -3.782861   1.339094    -2.82   0.005    -6.407437   -1.158284
        Missouri  |   1.925948   .8607486     2.24   0.025      .238912    3.612984
         Montana  |   .5291958   1.237838     0.43   0.669    -1.896921    2.955313
        Nebraska  |  -2.967736   1.067892    -2.78   0.005    -5.060765   -.8747066
          Nevada  |  -3.076675   .9916647    -3.10   0.002    -5.020302   -1.133048
      New Jersey  |   .0363105   .6552452     0.06   0.956    -1.247946    1.320567
      New Mexico  |  -.6917342   .8893629    -0.78   0.437    -2.434854    1.051385
        New York  |   .3396021   .6265597     0.54   0.588    -.8884323    1.567637
    North Dakota  |  -.1830154   1.177153    -0.16   0.876    -2.490193    2.124163
            Ohio  |    .938876   .6577488     1.43   0.153    -.3502879     2.22804
    Pennsylvania  |  -.1879798   .6416484    -0.29   0.770    -1.445588    1.069628
    Rhode Island  |          0  (empty)
           Texas  |  -3.748404   .8543731    -4.39   0.000    -5.422945   -2.073864
        Virginia  |   .8238738   .8036723     1.03   0.305    -.7512949    2.399043
      Washington  |    .951973   .8674039     1.10   0.272    -.7481073    2.652053
   West Virginia  |   1.072153   .9797186     1.09   0.274    -.8480606    2.992366
       Wisconsin  |   1.740377   1.146207     1.52   0.129    -.5061479    3.986902
         Wyoming  |          0  (empty)
                  |
            _cons |   1.628405   1.834628     0.89   0.375    -1.967399    5.224209
-----------------------------------------------------------------------------------

. 
. ///: Figure 6, right pane
> margins courttot, at(know=(.0 .1 to 1)) plot

Predictive margins                              Number of obs     =      6,799
Model VCE    : OIM

Expression   : Pr(voteinc), predict()

1._at        : know            =           0

2._at        : know            =          .1

3._at        : know            =          .2

4._at        : know            =          .3

5._at        : know            =          .4

6._at        : know            =          .5

7._at        : know            =          .6

8._at        : know            =          .7

9._at        : know            =          .8

10._at       : know            =          .9

11._at       : know            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at#courttot |
        1 0  |   .7464245   .0718222    10.39   0.000     .6056556    .8871935
        1 1  |   .7641567   .0540841    14.13   0.000     .6581539    .8701595
        1 2  |   .7947053   .0513002    15.49   0.000     .6941587    .8952519
        2 0  |   .7392728   .0638323    11.58   0.000     .6141638    .8643818
        2 1  |   .7926399   .0397678    19.93   0.000     .7146964    .8705834
        2 2  |   .8290224    .034052    24.35   0.000     .7622816    .8957632
        3 0  |   .7319053   .0560081    13.07   0.000     .6221315    .8416791
        3 1  |    .815725   .0284948    28.63   0.000     .7598762    .8715739
        3 2  |    .853895   .0220936    38.65   0.000     .8105924    .8971975
        4 0  |   .7242229   .0483872    14.97   0.000     .6293857    .8190601
        4 1  |   .8341462   .0199937    41.72   0.000     .7949592    .8733332
        4 2  |   .8715822   .0143101    60.91   0.000      .843535    .8996295
        5 0  |   .7161923    .041146    17.41   0.000     .6355477     .796837
        5 1  |   .8486952   .0138073    61.47   0.000     .8216334     .875757
        5 2  |   .8840794   .0094709    93.35   0.000     .8655168     .902642
        6 0  |   .7078256   .0346671    20.42   0.000     .6398793    .7757719
        6 1  |   .8601202   .0094547    90.97   0.000     .8415892    .8786511
        6 2  |   .8929542   .0065602   136.12   0.000     .8800964    .9058119
        7 0  |   .6991629    .029644    23.59   0.000     .6410617    .7572641
        7 1  |   .8690743   .0065253   133.19   0.000      .856285    .8818636
        7 2  |   .8993554   .0048705   184.65   0.000     .8898093    .9089014
        8 0  |   .6902607    .027079    25.49   0.000     .6371869    .7433345
        8 1  |   .8761003   .0047127   185.90   0.000     .8668635    .8853371
        8 2  |   .9040889   .0039851   226.87   0.000     .8962783    .9118995
        9 0  |   .6811838   .0277721    24.53   0.000     .6267515    .7356161
        9 1  |   .8816338   .0037845   232.96   0.000     .8742163    .8890513
        9 2  |   .9077042   .0036915   245.89   0.000     .9004691    .9149394
       10 0  |   .6719996   .0315326    21.31   0.000     .6101968    .7338024
       10 1  |   .8860173    .003494   253.59   0.000     .8791693    .8928653
       10 2  |   .9105691    .003854   236.27   0.000     .9030154    .9181228
       11 0  |   .6627747   .0374022    17.72   0.000     .5894676    .7360817
       11 1  |   .8895161   .0035691   249.23   0.000     .8825209    .8965114
       11 2  |   .9129262   .0043389   210.41   0.000     .9044222    .9214302
------------------------------------------------------------------------------

  Variables that uniquely identify margins: know courttot

. graph save Figure6right, replace
(file Figure6right.gph saved)

. 
. ///:Figure 7, right pane
> margins, dydx(courttot abort_same) at(know=(.0 .1 to 1)) plot

Average marginal effects                        Number of obs     =      6,799
Model VCE    : OIM

Expression   : Pr(voteinc), predict()
dy/dx w.r.t. : 1.courttot 2.courttot 1.abort_same

1._at        : know            =           0

2._at        : know            =          .1

3._at        : know            =          .2

4._at        : know            =          .3

5._at        : know            =          .4

6._at        : know            =          .5

7._at        : know            =          .6

8._at        : know            =          .7

9._at        : know            =          .8

10._at       : know            =          .9

11._at       : know            =           1

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
0.courttot    |  (base outcome)
--------------+----------------------------------------------------------------
1.courttot    |
          _at |
           1  |   .0177322     .08202     0.22   0.829    -.1430241    .1784884
           2  |   .0533671   .0693788     0.77   0.442    -.0826128     .189347
           3  |   .0838197   .0587661     1.43   0.154    -.0313598    .1989992
           4  |   .1099233   .0496295     2.21   0.027     .0126513    .2071952
           5  |   .1325029   .0416364     3.18   0.001      .050897    .2141088
           6  |   .1522946   .0348255     4.37   0.000     .0840378    .2205513
           7  |   .1699115   .0296949     5.72   0.000     .1117105    .2281124
           8  |   .1858396   .0271472     6.85   0.000      .132632    .2390472
           9  |     .20045   .0279173     7.18   0.000     .1457331    .2551669
          10  |   .2140177    .031762     6.74   0.000     .1517654      .27627
          11  |   .2267415   .0377023     6.01   0.000     .1528464    .3006366
--------------+----------------------------------------------------------------
2.courttot    |
          _at |
           1  |   .0482808    .082948     0.58   0.561    -.1142944    .2108559
           2  |   .0897496   .0689263     1.30   0.193    -.0453435    .2248427
           3  |   .1219896   .0582395     2.09   0.036     .0078424    .2361369
           4  |   .1473594   .0494356     2.98   0.003     .0504673    .2442514
           5  |   .1678871   .0417518     4.02   0.000     .0860551     .249719
           6  |   .1851286   .0351228     5.27   0.000     .1162892    .2539679
           7  |   .2001925    .030068     6.66   0.000     .1412602    .2591247
           8  |   .2138282   .0275297     7.77   0.000     .1598709    .2677854
           9  |   .2265205   .0282788     8.01   0.000     .1710951    .2819458
          10  |   .2385695   .0321088     7.43   0.000     .1756374    .3015015
          11  |   .2501515   .0380611     6.57   0.000     .1755531      .32475
--------------+----------------------------------------------------------------
0.abort_same  |  (base outcome)
--------------+----------------------------------------------------------------
1.abort_same  |
          _at |
           1  |  -.0299165   .0580296    -0.52   0.606    -.1436524    .0838194
           2  |  -.0134298   .0401953    -0.33   0.738    -.0922112    .0653515
           3  |  -.0025117   .0272708    -0.09   0.927    -.0559614     .050938
           4  |   .0043127   .0182973     0.24   0.814    -.0315494    .0401748
           5  |   .0083659   .0122318     0.68   0.494    -.0156079    .0323397
           6  |   .0106576   .0082272     1.30   0.195    -.0054674    .0267826
           7  |   .0118873   .0056915     2.09   0.037     .0007322    .0230425
           8  |   .0125094   .0042407     2.95   0.003     .0041978     .020821
           9  |   .0128052   .0035907     3.57   0.000     .0057676    .0198428
          10  |   .0129418   .0034565     3.74   0.000     .0061672    .0197164
          11  |    .013014   .0035823     3.63   0.000     .0059927    .0200352
-------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

  Variables that uniquely identify margins: know _deriv

. graph save Figure7right, replace
(file Figure7right.gph saved)

. 
. ///: Online Supplement for Study 2
> 
. ///:Summary statistics. Table 3. 
> 
. sum voteinc courttot ng_same bk_same abort_same know incdist pidinc ///
> gender white edu age income if voteinc!=.

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     voteinc |     21,648      .57682    .4940749          0          1
    courttot |     21,648    1.061853    .8511465          0          2
     ng_same |     21,609    .5055301     .499981          0          1
     bk_same |     21,622     .557904    .4966473          0          1
  abort_same |     21,616    .4300981    .4951011          0          1
-------------+---------------------------------------------------------
        know |     21,648    .8425259    .2474887          0          1
     incdist |     19,508    2.395786    1.959046          0          6
      pidinc |     21,437    4.326212    2.269304          1          7
      gender |     21,648     1.53155    .4990151          1          2
       white |     21,648    .8077882    .3940477          0          1
-------------+---------------------------------------------------------
        educ |     21,648    3.941103    1.503048          1          6
         age |     21,648    53.46808    16.80597         19         96
      income |     19,456    6.882401    3.291141          1         16

. 
. ///: Correlation between Congruence Items. Table 4. 
> 
. corr ng_same bk_same courttot incdist pidinc abort_same
(obs=28,328)

             |  ng_same  bk_same courttot  incdist   pidinc abort_~e
-------------+------------------------------------------------------
     ng_same |   1.0000
     bk_same |   0.4245   1.0000
    courttot |   0.8448   0.8431   1.0000
     incdist |  -0.3627  -0.6379  -0.5924   1.0000
      pidinc |   0.3793   0.7202   0.6509  -0.6642   1.0000
  abort_same |   0.2298   0.3049   0.3167  -0.2914   0.2999   1.0000


. 
. 
. ///Congruence by Party, Member of the Mass Public. Table 5. 
> gen pid3 = .
(60,000 missing values generated)

. replace pid3=1 if pid7 == 1 | pid7==2 | pid7==3
(27,607 real changes made)

. replace pid3=2 if pid7==4
(8,532 real changes made)

. replace pid3=3 if pid7==5 | pid7==6 |pid7==7
(21,720 real changes made)

. 
. by pid3, sort: summarize courtt if voteinc !=.

-----------------------------------------------------------------------------------------------------------------------------------------
-> pid3 = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    courttot |     10,921    1.502243    .6805914          0          2

-----------------------------------------------------------------------------------------------------------------------------------------
-> pid3 = 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    courttot |      2,241    .9848282     .814896          0          2

-----------------------------------------------------------------------------------------------------------------------------------------
-> pid3 = 3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    courttot |      8,275    .5025982    .7204575          0          2

-----------------------------------------------------------------------------------------------------------------------------------------
-> pid3 = .

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    courttot |        211    1.018957    .7619654          0          2


. 
. ///: Analysis of indivdual nominees. All. Table 7, Column 1. 
> logit voteinc (i.ng_same i.bk_same abort_same)##c.know c.incdist01 ///
> pidinc01 gender white educ age income i.inputstate if verified==1

Iteration 0:   log likelihood = -8992.9083  
Iteration 1:   log likelihood = -1881.8713  
Iteration 2:   log likelihood = -1557.2264  
Iteration 3:   log likelihood = -1523.2873  
Iteration 4:   log likelihood = -1522.9011  
Iteration 5:   log likelihood = -1522.9011  

Logistic regression                             Number of obs     =     13,194
                                                LR chi2(40)       =   14940.01
                                                Prob > chi2       =     0.0000
Log likelihood = -1522.9011                     Pseudo R2         =     0.8307

-----------------------------------------------------------------------------------
          voteinc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.ng_same |  -.1514063   .3376709    -0.45   0.654    -.8132291    .5104166
        1.bk_same |   .9707458   .3416686     2.84   0.004     .3010876    1.640404
     1.abort_same |   .0855255   .3411662     0.25   0.802     -.583148    .7541989
             know |  -1.374873   .3617576    -3.80   0.000    -2.083904   -.6658409
                  |
   ng_same#c.know |
               1  |   .5734698   .4119818     1.39   0.164    -.2339998    1.380939
                  |
   bk_same#c.know |
               1  |   2.958463   .4075841     7.26   0.000     2.159613    3.757313
                  |
abort_same#c.know |
               1  |   .6724827   .4034517     1.67   0.096     -.118268    1.463233
                  |
        incdist01 |  -4.280522   .2136168   -20.04   0.000    -4.699203   -3.861841
         pidinc01 |     4.4636   .1804748    24.73   0.000     4.109875    4.817324
           gender |  -.0962278     .10506    -0.92   0.360    -.3021415     .109686
            white |  -.5193476   .1380446    -3.76   0.000    -.7899101   -.2487851
             educ |  -.0383772   .0373431    -1.03   0.304    -.1115683    .0348138
              age |   .0119256   .0033495     3.56   0.000     .0053606    .0184906
           income |  -.0182882   .0168029    -1.09   0.276    -.0512213    .0146449
                  |
       inputstate |
        Delaware  |  -.5177549   .7842667    -0.66   0.509    -2.054889     1.01938
         Florida  |  -.3542466   .3655165    -0.97   0.332    -1.070646    .3621526
          Hawaii  |  -1.421595   .6783594    -2.10   0.036    -2.751155   -.0920351
         Indiana  |   .5825851   .4229887     1.38   0.168    -.2464575    1.411628
        Maryland  |   .3298261   .4643873     0.71   0.478    -.5803562    1.240008
   Massachusetts  |  -.1330526   .4232117    -0.31   0.753    -.9625324    .6964271
        Michigan  |  -.4838597    .394765    -1.23   0.220    -1.257585    .2898654
       Minnesota  |   .9442076   .4350951     2.17   0.030     .0914368    1.796978
     Mississippi  |  -.2890827    .574634    -0.50   0.615    -1.415345    .8371792
        Missouri  |   .3525884   .4113484     0.86   0.391    -.4536396    1.158816
         Montana  |   .3413244   .5949663     0.57   0.566    -.8247881    1.507437
        Nebraska  |   -1.20955   .5540544    -2.18   0.029    -2.295477   -.1236235
          Nevada  |  -.4548276   .5115194    -0.89   0.374    -1.457387    .5477319
      New Jersey  |  -.3955693   .4174963    -0.95   0.343    -1.213847    .4227084
      New Mexico  |  -.7516367   .5789464    -1.30   0.194    -1.886351    .3830773
        New York  |   .6464471   .3821261     1.69   0.091    -.1025063      1.3954
    North Dakota  |   .3068845   .6851594     0.45   0.654    -1.036003    1.649772
            Ohio  |   .8094869   .3801772     2.13   0.033     .0643532    1.554621
    Pennsylvania  |   .7346381   .3964808     1.85   0.064      -.04245    1.511726
    Rhode Island  |   1.480872   .6797637     2.18   0.029     .1485593    2.813184
           Texas  |  -1.508858   .3942913    -3.83   0.000    -2.281655   -.7360613
        Virginia  |   .3603175   .4147494     0.87   0.385    -.4525765    1.173211
      Washington  |  -.1829103   .4264598    -0.43   0.668    -1.018756    .6529355
   West Virginia  |  -.0553421   .4742496    -0.12   0.907    -.9848543      .87417
       Wisconsin  |    .883565   .4385843     2.01   0.044     .0239556    1.743175
         Wyoming  |  -3.435258   .7837935    -4.38   0.000    -4.971465   -1.899051
                  |
            _cons |  -.9490346   .5711548    -1.66   0.097    -2.068477    .1704082
-----------------------------------------------------------------------------------

. 
. ///: Figure 3, left most panel. All. 
> margins, dydx(ng_same bk_same abort_same) at(know=(.0 .1 to 1)) plot

Average marginal effects                        Number of obs     =     13,194
Model VCE    : OIM

Expression   : Pr(voteinc), predict()
dy/dx w.r.t. : 1.ng_same 1.bk_same 1.abort_same

1._at        : know            =           0

2._at        : know            =          .1

3._at        : know            =          .2

4._at        : know            =          .3

5._at        : know            =          .4

6._at        : know            =          .5

7._at        : know            =          .6

8._at        : know            =          .7

9._at        : know            =          .8

10._at       : know            =          .9

11._at       : know            =           1

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
0.ng_same     |  (base outcome)
--------------+----------------------------------------------------------------
1.ng_same     |
          _at |
           1  |  -.0103991   .0230341    -0.45   0.652    -.0555452    .0347469
           2  |  -.0059309   .0187599    -0.32   0.752    -.0426996    .0308378
           3  |   -.002123   .0150524    -0.14   0.888    -.0316252    .0273792
           4  |   .0010939   .0118767     0.09   0.927    -.0221841    .0243718
           5  |   .0037897   .0092043     0.41   0.681    -.0142504    .0218297
           6  |   .0060322   .0070239     0.86   0.390    -.0077345    .0197988
           7  |   .0078846   .0053571     1.47   0.141    -.0026151    .0183844
           8  |   .0094049   .0042678     2.20   0.028     .0010402    .0177696
           9  |   .0106446   .0038135     2.79   0.005     .0031703    .0181188
          10  |    .011649   .0039116     2.98   0.003     .0039823    .0193157
          11  |   .0124572   .0043377     2.87   0.004     .0039556    .0209589
--------------+----------------------------------------------------------------
0.bk_same     |  (base outcome)
--------------+----------------------------------------------------------------
1.bk_same     |
          _at |
           1  |   .0846511   .0307854     2.75   0.006     .0243128    .1449894
           2  |   .1085496    .027406     3.96   0.000     .0548349    .1622644
           3  |   .1317517   .0242466     5.43   0.000     .0842292    .1792742
           4  |   .1543655   .0213173     7.24   0.000     .1125845    .1961466
           5  |   .1764913   .0186686     9.45   0.000     .1399015    .2130812
           6  |   .1982174   .0164123    12.08   0.000     .1660498    .2303849
           7  |   .2196174   .0147376    14.90   0.000     .1907322    .2485027
           8  |   .2407492   .0138923    17.33   0.000     .2135208    .2679776
           9  |   .2616539   .0140775    18.59   0.000     .2340625    .2892452
          10  |   .2823562   .0153077    18.45   0.000     .2523537    .3123588
          11  |   .3028658   .0174055    17.40   0.000     .2687516    .3369799
--------------+----------------------------------------------------------------
0.abort_same  |  (base outcome)
--------------+----------------------------------------------------------------
1.abort_same  |
          _at |
           1  |   .0060287   .0240389     0.25   0.802    -.0410867    .0531441
           2  |   .0098896   .0196409     0.50   0.615    -.0286058     .048385
           3  |   .0130653   .0158395     0.82   0.409    -.0179795      .04411
           4  |   .0156392   .0125952     1.24   0.214    -.0090468    .0403253
           5  |    .017693   .0098743     1.79   0.073    -.0016604    .0370463
           6  |   .0193035   .0076586     2.52   0.012      .004293     .034314
           7  |   .0205414   .0059559     3.45   0.001      .008868    .0322147
           8  |   .0214699   .0048058     4.47   0.000     .0120507    .0308891
           9  |   .0221447   .0042442     5.22   0.000     .0138263    .0304631
          10  |   .0226134   .0042112     5.37   0.000     .0143597    .0308672
          11  |   .0229166   .0045301     5.06   0.000     .0140378    .0317955
-------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

  Variables that uniquely identify margins: know _deriv

. graph save AppendixFigure3left, replace
(file AppendixFigure3left.gph saved)

. 
. ///: Analysis of individual nominees. Republicans. Table 7, Column 2.
> logit voteinc (i.ng_same i.bk_same abort_same)##c.know c.incdist ///
>  pidinc01 gender white educ age income i.inputstate if verified==1 & pid7<4

note: 44.inputstate != 0 predicts success perfectly
      44.inputstate dropped and 40 obs not used

note: 56.inputstate != 0 predicts failure perfectly
      56.inputstate dropped and 17 obs not used

Iteration 0:   log likelihood = -2457.7499  
Iteration 1:   log likelihood =  -634.9313  
Iteration 2:   log likelihood = -504.35069  
Iteration 3:   log likelihood = -377.29432  
Iteration 4:   log likelihood = -369.92954  
Iteration 5:   log likelihood = -369.71886  
Iteration 6:   log likelihood = -369.71867  
Iteration 7:   log likelihood = -369.71867  

Logistic regression                             Number of obs     =      6,790
                                                LR chi2(38)       =    4176.06
                                                Prob > chi2       =     0.0000
Log likelihood = -369.71867                     Pseudo R2         =     0.8496

-----------------------------------------------------------------------------------
          voteinc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.ng_same |  -1.013093    .668015    -1.52   0.129    -2.322378    .2961924
        1.bk_same |   1.602175   .6610554     2.42   0.015       .30653     2.89782
     1.abort_same |  -.6123781   .6323389    -0.97   0.333    -1.851739    .6269833
             know |  -2.183102   .7309088    -2.99   0.003    -3.615657   -.7505468
                  |
   ng_same#c.know |
               1  |   1.365951   .8316791     1.64   0.101    -.2641105    2.996012
                  |
   bk_same#c.know |
               1  |    3.51787   .8217304     4.28   0.000     1.907308    5.128432
                  |
abort_same#c.know |
               1  |    1.59995   .7891772     2.03   0.043      .053191    3.146709
                  |
          incdist |  -.4782158   .0741302    -6.45   0.000    -.6235084   -.3329232
         pidinc01 |   3.703931   .8573743     4.32   0.000     2.023509    5.384354
           gender |  -.4424351   .2271821    -1.95   0.051    -.8877038    .0028336
            white |  -1.213695     .27831    -4.36   0.000    -1.759173   -.6682179
             educ |  -.0967559   .0792548    -1.22   0.222    -.2520925    .0585806
              age |   .0113791   .0071682     1.59   0.112    -.0026704    .0254286
           income |   -.021477   .0353375    -0.61   0.543    -.0907373    .0477833
                  |
       inputstate |
        Delaware  |  -1.380416   1.123091    -1.23   0.219    -3.581634    .8208018
         Florida  |  -.3084495   .6132547    -0.50   0.615    -1.510407    .8935077
          Hawaii  |  -2.629909   .9710285    -2.71   0.007     -4.53309   -.7267283
         Indiana  |   .9655623   .9206548     1.05   0.294     -.838888    2.770013
        Maryland  |   .9687477   .8634155     1.12   0.262    -.7235156    2.661011
   Massachusetts  |   .3696968   .7492664     0.49   0.622    -1.098838    1.838232
        Michigan  |  -.1400198   .6790046    -0.21   0.837    -1.470844    1.190805
       Minnesota  |   1.658983   .8852263     1.87   0.061    -.0760283    3.393995
     Mississippi  |  -2.604036   1.459282    -1.78   0.074    -5.464177    .2561041
        Missouri  |   2.006217   .8895678     2.26   0.024     .2626963    3.749738
         Montana  |    .931995   1.287103     0.72   0.469    -1.590681    3.454671
        Nebraska  |  -.9231887     1.1413    -0.81   0.419    -3.160097    1.313719
          Nevada  |  -2.351769   1.231479    -1.91   0.056    -4.765423    .0618846
      New Jersey  |   .2773829   .6984449     0.40   0.691    -1.091544     1.64631
      New Mexico  |   .0232284   .9946967     0.02   0.981    -1.926341    1.972798
        New York  |   .6079563   .6690844     0.91   0.364     -.703425    1.919338
    North Dakota  |   .2364887   1.614302     0.15   0.884    -2.927485    3.400463
            Ohio  |   1.266664    .696304     1.82   0.069    -.0980664    2.631395
    Pennsylvania  |   .1999816   .6841075     0.29   0.770    -1.140844    1.540808
    Rhode Island  |          0  (empty)
           Texas  |  -2.929869    .965463    -3.03   0.002    -4.822141   -1.037596
        Virginia  |   1.105193   .8523368     1.30   0.195    -.5653561    2.775743
      Washington  |    1.38035   .9062958     1.52   0.128    -.3959575    3.156657
   West Virginia  |   .6889282   .9724952     0.71   0.479    -1.217127    2.594984
       Wisconsin  |   1.974241   1.200452     1.64   0.100    -.3786023    4.327083
         Wyoming  |          0  (empty)
                  |
            _cons |   .6971106   1.326578     0.53   0.599    -1.902934    3.297155
-----------------------------------------------------------------------------------

. 
.  ///:Figure 3, middle panel. Republicans.
> margins, dydx(ng_same bk_same abort_same) at(know=(.0 .1 to 1)) plot

Average marginal effects                        Number of obs     =      6,790
Model VCE    : OIM

Expression   : Pr(voteinc), predict()
dy/dx w.r.t. : 1.ng_same 1.bk_same 1.abort_same

1._at        : know            =           0

2._at        : know            =          .1

3._at        : know            =          .2

4._at        : know            =          .3

5._at        : know            =          .4

6._at        : know            =          .5

7._at        : know            =          .6

8._at        : know            =          .7

9._at        : know            =          .8

10._at       : know            =          .9

11._at       : know            =           1

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
0.ng_same     |  (base outcome)
--------------+----------------------------------------------------------------
1.ng_same     |
          _at |
           1  |  -.0547675     .03529    -1.55   0.121    -.1239346    .0143996
           2  |  -.0391906   .0255369    -1.53   0.125     -.089242    .0108609
           3  |  -.0273685   .0182415    -1.50   0.134    -.0631212    .0083842
           4  |  -.0185725   .0129577    -1.43   0.152    -.0439692    .0068242
           5  |  -.0120928   .0092102    -1.31   0.189    -.0301445    .0059588
           6  |  -.0073112   .0065983    -1.11   0.268    -.0202436    .0056211
           7  |    -.00373   .0048487    -0.77   0.442    -.0132333    .0057733
           8  |  -.0009729   .0038207    -0.25   0.799    -.0084613    .0065155
           9  |   .0012289   .0034371     0.36   0.721    -.0055077    .0079656
          10  |   .0030582   .0035577     0.86   0.390    -.0039148    .0100311
          11  |   .0046325    .003976     1.17   0.244    -.0031602    .0124253
--------------+----------------------------------------------------------------
0.bk_same     |  (base outcome)
--------------+----------------------------------------------------------------
1.bk_same     |
          _at |
           1  |   .1536007   .0697719     2.20   0.028     .0168503    .2903511
           2  |   .1739859   .0609519     2.85   0.004     .0545224    .2934495
           3  |   .1908206   .0530152     3.60   0.000     .0869127    .2947285
           4  |   .2049545   .0457303     4.48   0.000     .1153247    .2945843
           5  |   .2171795   .0390997     5.55   0.000     .1405455    .2938136
           6  |   .2281709   .0333819     6.84   0.000     .1627436    .2935982
           7  |   .2384626   .0291047     8.19   0.000     .1814185    .2955067
           8  |   .2484456    .026977     9.21   0.000     .1955715    .3013196
           9  |   .2583794   .0274948     9.40   0.000     .2044906    .3122683
          10  |    .268412   .0304798     8.81   0.000     .2086728    .3281512
          11  |   .2786013   .0352356     7.91   0.000     .2095408    .3476618
--------------+----------------------------------------------------------------
0.abort_same  |  (base outcome)
--------------+----------------------------------------------------------------
1.abort_same  |
          _at |
           1  |  -.0365966   .0399378    -0.92   0.359    -.1148734    .0416801
           2  |  -.0222723   .0285678    -0.78   0.436    -.0782641    .0337195
           3  |  -.0118223   .0201168    -0.59   0.557    -.0512504    .0276058
           4  |  -.0044084   .0140497    -0.31   0.754    -.0319453    .0231285
           5  |   .0007634   .0098019     0.08   0.938    -.0184479    .0199747
           6  |   .0043683   .0068961     0.63   0.526    -.0091478    .0178844
           7  |   .0069349   .0049963     1.39   0.165    -.0028576    .0167274
           8  |   .0088487   .0039023     2.27   0.023     .0012003    .0164972
           9  |    .010373   .0034745     2.99   0.003     .0035631    .0171829
          10  |   .0116752   .0035287     3.31   0.001      .004759    .0185913
          11  |   .0128536   .0038552     3.33   0.001     .0052977    .0204096
-------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

  Variables that uniquely identify margins: know _deriv

. graph save AppendixFigure3middle, replace
(file AppendixFigure3middle.gph saved)

. 
. ///: Analysis of individual nominees. Democrats. Table 7, Column 3. 
> logit voteinc (i.ng_same i.bk_same abort_same)##c.know c.incdist ///
> pidinc01 gender white educ age income i.inputstate if verified==1 & pid7>4

note: 15.inputstate != 0 predicts failure perfectly
      15.inputstate dropped and 27 obs not used

note: 28.inputstate != 0 predicts success perfectly
      28.inputstate dropped and 79 obs not used

note: 35.inputstate != 0 predicts failure perfectly
      35.inputstate dropped and 63 obs not used

Iteration 0:   log likelihood = -2316.0797  
Iteration 1:   log likelihood = -926.10907  
Iteration 2:   log likelihood = -748.94351  
Iteration 3:   log likelihood =  -722.4714  
Iteration 4:   log likelihood =  -722.1095  
Iteration 5:   log likelihood = -722.10944  

Logistic regression                             Number of obs     =      5,011
                                                LR chi2(37)       =    3187.94
                                                Prob > chi2       =     0.0000
Log likelihood = -722.10944                     Pseudo R2         =     0.6882

-----------------------------------------------------------------------------------
          voteinc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.ng_same |  -.1244948   .5180404    -0.24   0.810    -1.139835    .8908457
        1.bk_same |   .4395412   .5480879     0.80   0.423    -.6346914    1.513774
     1.abort_same |   .8566276   .5224734     1.64   0.101    -.1674014    1.880657
             know |  -.5488313   .5281497    -1.04   0.299    -1.583986     .486323
                  |
   ng_same#c.know |
               1  |   .5082965   .6393472     0.80   0.427     -.744801    1.761394
                  |
   bk_same#c.know |
               1  |   3.063325   .6509323     4.71   0.000     1.787521    4.339128
                  |
abort_same#c.know |
               1  |  -.5001096   .6197954    -0.81   0.420    -1.714886     .714667
                  |
          incdist |   -.831689   .0522257   -15.92   0.000    -.9340494   -.7293286
         pidinc01 |   2.740661   .5814754     4.71   0.000      1.60099    3.880332
           gender |  -.0234934   .1527778    -0.15   0.878    -.3229324    .2759455
            white |  -.6907143   .2307391    -2.99   0.003    -1.142955   -.2384741
             educ |  -.0087984   .0546537    -0.16   0.872    -.1159177     .098321
              age |   .0184555   .0049139     3.76   0.000     .0088245    .0280865
           income |  -.0003381   .0251533    -0.01   0.989    -.0496378    .0489615
                  |
       inputstate |
        Delaware  |   .8735526    .973673     0.90   0.370    -1.034811    2.781917
         Florida  |  -.6667603    .560643    -1.19   0.234      -1.7656    .4320796
          Hawaii  |          0  (empty)
         Indiana  |   .4696293   .6090164     0.77   0.441     -.724021     1.66328
        Maryland  |   .1223582   .6757907     0.18   0.856    -1.202167    1.446884
   Massachusetts  |  -.7055312   .7318655    -0.96   0.335    -2.139961    .7288988
        Michigan  |  -.4523777   .6130534    -0.74   0.461     -1.65394    .7491849
       Minnesota  |   .7136365   .6244984     1.14   0.253    -.5103579    1.937631
     Mississippi  |          0  (empty)
        Missouri  |  -.5552081   .6495392    -0.85   0.393    -1.828282    .7178654
         Montana  |   .2874332   .9073138     0.32   0.751    -1.490869    2.065736
        Nebraska  |  -.0152381   .9252659    -0.02   0.987    -1.828726     1.79825
          Nevada  |   .5734765   .9059126     0.63   0.527     -1.20208    2.349033
      New Jersey  |   -1.18909    .706444    -1.68   0.092    -2.573695    .1955145
      New Mexico  |          0  (empty)
        New York  |   .7510943   .5649407     1.33   0.184    -.3561692    1.858358
    North Dakota  |   .1833961    .883842     0.21   0.836    -1.548902    1.915695
            Ohio  |   .5116743   .5680592     0.90   0.368    -.6017013     1.62505
    Pennsylvania  |   .6209394     .58193     1.07   0.286    -.5196224    1.761501
    Rhode Island  |   1.025558   1.098233     0.93   0.350    -1.126939    3.178055
           Texas  |  -.5591605   .6963201    -0.80   0.422    -1.923923    .8056018
        Virginia  |   .0079366   .6130071     0.01   0.990    -1.193535    1.209408
      Washington  |  -.6931033   .6825586    -1.02   0.310    -2.030894    .6446871
   West Virginia  |  -.0893436   .6868573    -0.13   0.897    -1.435559    1.256872
       Wisconsin  |    .794635   .6178572     1.29   0.198    -.4163429    2.005613
         Wyoming  |  -1.966242   1.079952    -1.82   0.069    -4.082909    .1504255
                  |
            _cons |  -1.048764   .8466954    -1.24   0.215    -2.708256    .6107285
-----------------------------------------------------------------------------------

. 
. ///: Figure 3, right most panel. Democrats.
> margins, dydx(ng_same bk_same abort_same) at(know=(.0 .1 to 1)) plot

Average marginal effects                        Number of obs     =      5,011
Model VCE    : OIM

Expression   : Pr(voteinc), predict()
dy/dx w.r.t. : 1.ng_same 1.bk_same 1.abort_same

1._at        : know            =           0

2._at        : know            =          .1

3._at        : know            =          .2

4._at        : know            =          .3

5._at        : know            =          .4

6._at        : know            =          .5

7._at        : know            =          .6

8._at        : know            =          .7

9._at        : know            =          .8

10._at       : know            =          .9

11._at       : know            =           1

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
0.ng_same     |  (base outcome)
--------------+----------------------------------------------------------------
1.ng_same     |
          _at |
           1  |  -.0069479   .0288048    -0.24   0.809    -.0634044    .0495085
           2  |  -.0039972    .024776    -0.16   0.872    -.0525573    .0445629
           3  |  -.0012031   .0209733    -0.06   0.954      -.04231    .0399038
           4  |     .00143   .0174254     0.08   0.935    -.0327232    .0355832
           5  |   .0038974   .0141806     0.27   0.783    -.0238961    .0316909
           6  |   .0061941   .0113257     0.55   0.584    -.0160039    .0283922
           7  |   .0083155   .0090194     0.92   0.357    -.0093621    .0259931
           8  |   .0102581   .0075215     1.36   0.173    -.0044838    .0249999
           9  |   .0120203   .0070942     1.69   0.090    -.0018841    .0259248
          10  |   .0136035   .0076934     1.77   0.077    -.0014752    .0286822
          11  |   .0150115    .008944     1.68   0.093    -.0025184    .0325415
--------------+----------------------------------------------------------------
0.bk_same     |  (base outcome)
--------------+----------------------------------------------------------------
1.bk_same     |
          _at |
           1  |   .0274317   .0354325     0.77   0.439    -.0420148    .0968782
           2  |   .0481981   .0336941     1.43   0.153     -.017841    .1142373
           3  |   .0704886   .0318683     2.21   0.027     .0080279    .1329493
           4  |   .0944464   .0298842     3.16   0.002     .0358745    .1530183
           5  |   .1202016   .0277213     4.34   0.000     .0658688    .1745345
           6  |   .1478624   .0254654     5.81   0.000     .0979511    .1977736
           7  |   .1775045   .0233971     7.59   0.000     .1316471     .223362
           8  |   .2091613   .0220981     9.47   0.000     .1658499    .2524728
           9  |   .2428124   .0224217    10.83   0.000     .1988667     .286758
          10  |   .2783735   .0250711    11.10   0.000      .229235    .3275119
          11  |   .3156887   .0300927    10.49   0.000     .2567082    .3746693
--------------+----------------------------------------------------------------
0.abort_same  |  (base outcome)
--------------+----------------------------------------------------------------
1.abort_same  |
          _at |
           1  |   .0562078   .0360078     1.56   0.119    -.0143661    .1267818
           2  |   .0506912   .0306143     1.66   0.098    -.0093118    .1106942
           3  |   .0454182   .0256647     1.77   0.077    -.0048837    .0957201
           4  |   .0404232   .0212004     1.91   0.057    -.0011288    .0819752
           5  |   .0357277   .0172695     2.07   0.039     .0018802    .0695753
           6  |   .0313428   .0139395     2.25   0.025     .0040219    .0586636
           7  |   .0272706   .0113127     2.41   0.016     .0050981    .0494432
           8  |   .0235074   .0095257     2.47   0.014     .0048374    .0421775
           9  |   .0200446   .0086803     2.31   0.021     .0030316    .0370576
          10  |   .0168706   .0087045     1.94   0.053    -.0001899     .033931
          11  |   .0139718    .009332     1.50   0.134    -.0043186    .0322622
-------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

  Variables that uniquely identify margins: know _deriv

. graph save AppendixFigure3right, replace
(file AppendixFigure3right.gph saved)

. 
. ///: Congruence of Judicial Confirmation Votes and Partisan Congruence Table 8
> logit voteinc (i.courttot )##c.pidinc abort_same c.incdist know gender white educ age /// 
> income i.inputstate 

Iteration 0:   log likelihood = -11826.819  
Iteration 1:   log likelihood = -3102.2639  
Iteration 2:   log likelihood = -2742.9223  
Iteration 3:   log likelihood = -2708.3593  
Iteration 4:   log likelihood = -2707.9025  
Iteration 5:   log likelihood = -2707.9023  

Logistic regression                             Number of obs     =     17,380
                                                LR chi2(39)       =   18237.83
                                                Prob > chi2       =     0.0000
Log likelihood = -2707.9023                     Pseudo R2         =     0.7710

-----------------------------------------------------------------------------------
          voteinc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         courttot |
               1  |   .9278619    .200887     4.62   0.000     .5341306    1.321593
               2  |   2.241294   .2679621     8.36   0.000     1.716098     2.76649
                  |
           pidinc |   .7160452   .0378195    18.93   0.000     .6419204    .7901699
                  |
courttot#c.pidinc |
               1  |   .2091172   .0494093     4.23   0.000     .1122769    .3059576
               2  |   .2522818   .0634431     3.98   0.000     .1279356     .376628
                  |
       abort_same |   .5972777   .0819779     7.29   0.000      .436604    .7579515
          incdist |  -.7493936   .0258926   -28.94   0.000    -.8001422    -.698645
             know |   .8273823   .1623872     5.10   0.000     .5091093    1.145655
           gender |   -.065764   .0751097    -0.88   0.381    -.2129763    .0814482
            white |  -.3769879   .0973776    -3.87   0.000    -.5678445   -.1861314
             educ |   .0521625   .0272429     1.91   0.056    -.0012325    .1055575
              age |   .0044135   .0023713     1.86   0.063    -.0002343    .0090612
           income |  -.0190979    .012352    -1.55   0.122    -.0433074    .0051116
                  |
       inputstate |
        Delaware  |  -.3863431   .5597375    -0.69   0.490    -1.483409    .7107223
         Florida  |  -.6791769   .2739685    -2.48   0.013    -1.216145   -.1422084
          Hawaii  |  -.9770122   .4818194    -2.03   0.043    -1.921361   -.0326636
         Indiana  |  -.2006649   .3094327    -0.65   0.517    -.8071419    .4058121
        Maryland  |   .3984658   .3398743     1.17   0.241    -.2676756    1.064607
   Massachusetts  |  -.3461974   .3126265    -1.11   0.268    -.9589342    .2665393
        Michigan  |  -.5037899   .2986428    -1.69   0.092    -1.089119    .0815391
       Minnesota  |    .393564   .3321147     1.19   0.236    -.2573689    1.044497
     Mississippi  |  -.6119412     .41222    -1.48   0.138    -1.419878     .195995
        Missouri  |  -.2038741   .3127082    -0.65   0.514     -.816771    .4090227
         Montana  |  -.0451858   .4682784    -0.10   0.923    -.9629946    .8726229
        Nebraska  |  -1.795939   .4201186    -4.27   0.000    -2.619357   -.9725221
          Nevada  |  -1.472076   .3699487    -3.98   0.000    -2.197162   -.7469898
      New Jersey  |  -.7039979   .3082715    -2.28   0.022    -1.308199   -.0997968
      New Mexico  |  -.6527584   .4414506    -1.48   0.139    -1.517986    .2124688
        New York  |   .3191478    .283888     1.12   0.261    -.2372625    .8755581
    North Dakota  |  -.5751274   .4626349    -1.24   0.214    -1.481875    .3316204
            Ohio  |   .4399082    .286596     1.53   0.125    -.1218096    1.001626
    Pennsylvania  |   .3227394   .3009012     1.07   0.283     -.267016    .9124948
    Rhode Island  |   .6581722   .5471507     1.20   0.229    -.4142234    1.730568
           Texas  |   -2.10945   .2927334    -7.21   0.000    -2.683197   -1.535703
        Virginia  |   .1670464   .3067624     0.54   0.586    -.4341969    .7682897
      Washington  |  -.5298808   .3156416    -1.68   0.093    -1.148527    .0887653
   West Virginia  |  -.6048473   .3447718    -1.75   0.079    -1.280588    .0708929
       Wisconsin  |    .504879   .3258395     1.55   0.121    -.1337546    1.143513
         Wyoming  |  -2.853966    .590757    -4.83   0.000    -4.011828   -1.696103
                  |
            _cons |  -2.866165   .4003966    -7.16   0.000    -3.650928   -2.081402
-----------------------------------------------------------------------------------

. 
. ///: Congruence of Judicial Confirmation Votes and Partisan Congruence Figure 4
> margins courttot, at(pidinc=(1(1)7)) plot

Predictive margins                              Number of obs     =     17,380
Model VCE    : OIM

Expression   : Pr(voteinc), predict()

1._at        : pidinc          =           1

2._at        : pidinc          =           2

3._at        : pidinc          =           3

4._at        : pidinc          =           4

5._at        : pidinc          =           5

6._at        : pidinc          =           6

7._at        : pidinc          =           7

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at#courttot |
        1 0  |   .0846619    .008784     9.64   0.000     .0674456    .1018782
        1 1  |   .1908301   .0135265    14.11   0.000     .1643187    .2173416
        1 2  |    .382152   .0272391    14.03   0.000     .3287642    .4355397
        2 0  |   .1446245   .0101474    14.25   0.000      .124736     .164513
        2 1  |   .3163728   .0127502    24.81   0.000     .2913828    .3413628
        2 2  |   .5324916   .0200666    26.54   0.000     .4931618    .5718215
        3 0  |   .2277203    .010434    21.82   0.000     .2072701    .2481706
        3 1  |   .4592095    .009851    46.62   0.000     .4399019     .478517
        3 2  |    .672089   .0134058    50.13   0.000     .6458141    .6983638
        4 0  |   .3292545    .010975    30.00   0.000     .3077439    .3507651
        4 1  |   .5995547   .0083072    72.17   0.000     .5832729    .6158364
        4 2  |   .7905344   .0099488    79.46   0.000      .771035    .8100338
        5 0  |   .4399731   .0132853    33.12   0.000     .4139345    .4660117
        5 1  |   .7249446   .0092154    78.67   0.000     .7068827    .7430066
        5 2  |   .8807899   .0084941   103.69   0.000     .8641418     .897438
        6 0  |   .5503965     .01671    32.94   0.000     .5176454    .5831476
        6 1  |   .8280941   .0098743    83.86   0.000     .8087408    .8474475
        6 2  |   .9399538   .0066559   141.22   0.000     .9269084    .9529992
        7 0  |    .653858   .0198624    32.92   0.000     .6149283    .6927877
        7 1  |   .9037764    .008696   103.93   0.000     .8867325    .9208203
        7 2  |   .9728151   .0043568   223.28   0.000     .9642758    .9813543
------------------------------------------------------------------------------

  Variables that uniquely identify margins: pidinc courttot

. graph save AppendixFigure4, replace
(file AppendixFigure4.gph saved)

. 
. ///: Three-way Interaction Nominees x Knowledge x Party. Table 9
> 
. logit voteinc (i.courttot abort_same c.pidinc c.incdist)##c.know ///
> i.courttot##c.know##i.repdem gender white educ age income i.inputstate if verified==1

note: know omitted because of collinearity
Iteration 0:   log likelihood = -8190.3616  
Iteration 1:   log likelihood = -1627.7117  
Iteration 2:   log likelihood = -1342.7689  
Iteration 3:   log likelihood = -1287.7782  
Iteration 4:   log likelihood = -1285.8888  
Iteration 5:   log likelihood = -1285.8801  
Iteration 6:   log likelihood = -1285.8801  

Logistic regression                             Number of obs     =     12,044
                                                LR chi2(48)       =   13808.96
                                                Prob > chi2       =     0.0000
Log likelihood = -1285.8801                     Pseudo R2         =     0.8430

----------------------------------------------------------------------------------------
               voteinc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
              courttot |
                    1  |  -.1601898   .5100913    -0.31   0.753     -1.15995    .8395709
                    2  |   .3062407   .6821709     0.45   0.653     -1.03079    1.643271
                       |
          1.abort_same |   .4750049      .3745     1.27   0.205    -.2590015    1.209011
                pidinc |   1.002528    .138621     7.23   0.000     .7308354     1.27422
               incdist |  -.1810834   .1212428    -1.49   0.135    -.4187149    .0565481
                  know |   .9442599   .7198668     1.31   0.190    -.4666531    2.355173
                       |
       courttot#c.know |
                    1  |    1.79952   .6123566     2.94   0.003     .5993235    2.999717
                    2  |   3.535168   .8209302     4.31   0.000     1.926175    5.144162
                       |
     abort_same#c.know |
                    1  |   .2121837   .4447622     0.48   0.633    -.6595342    1.083901
                       |
       c.pidinc#c.know |  -.1784199   .1632263    -1.09   0.274    -.4983376    .1414978
                       |
      c.incdist#c.know |  -.7748984   .1490312    -5.20   0.000    -1.066994   -.4828026
                       |
                  know |          0  (omitted)
              2.repdem |  -1.402213   .8271152    -1.70   0.090    -3.023329     .218903
                       |
       courttot#repdem |
                  1 2  |   .4732977   .8597393     0.55   0.582     -1.21176    2.158356
                  2 2  |   .4349003   1.010781     0.43   0.667    -1.546194    2.415995
                       |
         repdem#c.know |
                    2  |   .5563724   .9940445     0.56   0.576    -1.391919    2.504664
                       |
courttot#repdem#c.know |
                  1 2  |   1.229378   1.047218     1.17   0.240    -.8231321    3.281888
                  2 2  |   .8823601   1.247065     0.71   0.479    -1.561842    3.326562
                       |
                gender |  -.1594776   .1147535    -1.39   0.165    -.3843902     .065435
                 white |  -.7429056   .1591042    -4.67   0.000    -1.054744   -.4310671
                  educ |   .0353583   .0406065     0.87   0.384     -.044229    .1149456
                   age |    .009239   .0035924     2.57   0.010     .0021981    .0162799
                income |  -.0009984   .0187051    -0.05   0.957    -.0376598     .035663
                       |
            inputstate |
             Delaware  |   -.031079   .9849078    -0.03   0.975    -1.961463    1.899305
              Florida  |  -.5085634   .4401855    -1.16   0.248    -1.371311    .3541843
               Hawaii  |  -2.774663   .7891974    -3.52   0.000    -4.321462   -1.227865
              Indiana  |   .1817192   .4767013     0.38   0.703    -.7525981    1.116037
             Maryland  |    .432307   .5386694     0.80   0.422    -.6234657     1.48808
        Massachusetts  |  -.3587815   .5191668    -0.69   0.490     -1.37633    .6587669
             Michigan  |  -.3807375   .4753207    -0.80   0.423    -1.312349    .5508739
            Minnesota  |    .754293   .5087789     1.48   0.138    -.2428953    1.751481
          Mississippi  |  -.7934737   .6488505    -1.22   0.221    -2.065197    .4782499
             Missouri  |   .1563829   .4815824     0.32   0.745    -.7875012    1.100267
              Montana  |    .509138   .7278749     0.70   0.484    -.9174707    1.935747
             Nebraska  |  -1.809583   .6267156    -2.89   0.004    -3.037923   -.5812433
               Nevada  |  -1.486555   .5633759    -2.64   0.008    -2.590751   -.3823585
           New Jersey  |  -.5228681   .4915791    -1.06   0.287    -1.486345    .4406093
           New Mexico  |  -1.361175   .6827298    -1.99   0.046    -2.699301   -.0230495
             New York  |   .7424663   .4566723     1.63   0.104     -.152595    1.637528
         North Dakota  |  -.6108193    .663055    -0.92   0.357    -1.910383    .6887445
                 Ohio  |   .6274683   .4564767     1.37   0.169    -.2672096    1.522146
         Pennsylvania  |   .3333082   .4701924     0.71   0.478     -.588252    1.254868
         Rhode Island  |   1.290891   .8379409     1.54   0.123    -.3514429    2.933225
                Texas  |  -2.212384   .4690842    -4.72   0.000    -3.131772   -1.292996
             Virginia  |   .2066977   .4900569     0.42   0.673    -.7537963    1.167192
           Washington  |   -.269605   .5084114    -0.53   0.596    -1.266073     .726863
        West Virginia  |  -.4362098   .5206601    -0.84   0.402    -1.456685    .5842651
            Wisconsin  |   .8551631   .5045585     1.69   0.090    -.1337533     1.84408
              Wyoming  |  -3.429554   .8159858    -4.20   0.000    -5.028857   -1.830251
                       |
                 _cons |  -3.049302   .8004497    -3.81   0.000    -4.618155   -1.480449
----------------------------------------------------------------------------------------

. 
. ///: Figure 5. 
> margins, dydx(courttot) at(know=(.0 .1 to 1)) by(repdem) post plot

Average marginal effects                        Number of obs     =     12,044
Model VCE    : OIM

Expression   : Pr(voteinc), predict()
dy/dx w.r.t. : 1.courttot 2.courttot
over         : repdem

1._at        : 1.repdem
                   know            =           0
               2.repdem
                   know            =           0

2._at        : 1.repdem
                   know            =          .1
               2.repdem
                   know            =          .1

3._at        : 1.repdem
                   know            =          .2
               2.repdem
                   know            =          .2

4._at        : 1.repdem
                   know            =          .3
               2.repdem
                   know            =          .3

5._at        : 1.repdem
                   know            =          .4
               2.repdem
                   know            =          .4

6._at        : 1.repdem
                   know            =          .5
               2.repdem
                   know            =          .5

7._at        : 1.repdem
                   know            =          .6
               2.repdem
                   know            =          .6

8._at        : 1.repdem
                   know            =          .7
               2.repdem
                   know            =          .7

9._at        : 1.repdem
                   know            =          .8
               2.repdem
                   know            =          .8

10._at       : 1.repdem
                   know            =          .9
               2.repdem
                   know            =          .9

11._at       : 1.repdem
                   know            =           1
               2.repdem
                   know            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.courttot   |  (base outcome)
-------------+----------------------------------------------------------------
1.courttot   |
  _at#repdem |
        1 1  |  -.0179836    .057397    -0.31   0.754    -.1304796    .0945124
        1 2  |   .0315995   .0710799     0.44   0.657    -.1077146    .1709136
        2 1  |   .0020603   .0471075     0.04   0.965    -.0902687    .0943892
        2 2  |   .0587393   .0609332     0.96   0.335    -.0606876    .1781663
        3 1  |   .0193334   .0382537     0.51   0.613    -.0556425    .0943093
        3 2  |   .0827652   .0518822     1.60   0.111    -.0189221    .1844525
        4 1  |   .0341815   .0307048     1.11   0.266    -.0259988    .0943617
        4 2  |   .1040137   .0437655     2.38   0.017     .0182349    .1897925
        5 1  |   .0469868   .0243133     1.93   0.053    -.0006664    .0946401
        5 2  |   .1228611   .0365069     3.37   0.001      .051309    .1944133
        6 1  |   .0581172   .0189885     3.06   0.002     .0209004     .095334
        6 2  |    .139686   .0302135     4.62   0.000     .0804685    .1989034
        7 1  |   .0678947   .0147676     4.60   0.000     .0389507    .0968388
        7 2  |   .1548411    .025291     6.12   0.000     .1052716    .2044106
        8 1  |   .0765842   .0118862     6.44   0.000     .0532877    .0998808
        8 2  |    .168637   .0224962     7.50   0.000     .1245453    .2127288
        9 1  |   .0843941   .0107123     7.88   0.000     .0633984    .1053898
        9 2  |   .1813342   .0225768     8.03   0.000     .1370846    .2255838
       10 1  |   .0914837   .0112969     8.10   0.000     .0693421    .1136253
       10 2  |   .1931412   .0254862     7.58   0.000     .1431892    .2430932
       11 1  |   .0979732    .013113     7.47   0.000     .0722722    .1236742
       11 2  |   .2042181   .0304028     6.72   0.000     .1446296    .2638066
-------------+----------------------------------------------------------------
2.courttot   |
  _at#repdem |
        1 1  |    .037563   .0862326     0.44   0.663    -.1314498    .2065757
        1 2  |   .0672484   .0694934     0.97   0.333    -.0689561    .2034529
        2 1  |   .0778045   .0764894     1.02   0.309    -.0721119     .227721
        2 2  |   .0977539      .0587     1.67   0.096     -.017296    .2128038
        3 1  |   .1147849   .0670712     1.71   0.087    -.0166722    .2462421
        3 2  |   .1225383   .0498616     2.46   0.014     .0248114    .2202652
        4 1  |   .1486771   .0578612     2.57   0.010     .0352712    .2620829
        4 2  |   .1429174   .0422963     3.38   0.001     .0600181    .2258167
        5 1  |   .1796993   .0489087     3.67   0.000       .08384    .2755586
        5 2  |   .1601007   .0356145     4.50   0.000     .0902976    .2299039
        6 1  |   .2080819   .0404924     5.14   0.000     .1287182    .2874455
        6 2  |   .1750764   .0297992     5.88   0.000     .1166711    .2334817
        7 1  |   .2340495   .0332192     7.05   0.000      .168941    .2991579
        7 2  |   .1885753   .0252373     7.47   0.000     .1391112    .2380395
        8 1  |   .2578159   .0281597     9.16   0.000     .2026239    .3130079
        8 2  |   .2010899   .0226938     8.86   0.000     .1566109    .2455689
        9 1  |   .2795849   .0266498    10.49   0.000     .2273523    .3318174
        9 2  |   .2129246   .0229155     9.29   0.000     .1680111     .257838
       10 1  |   .2995522   .0291604    10.27   0.000     .2423988    .3567055
       10 2  |   .2242563   .0258799     8.67   0.000     .1735326    .2749799
       11 1  |   .3179056   .0346839     9.17   0.000     .2499264    .3858848
       11 2  |   .2351861   .0308186     7.63   0.000     .1747827    .2955895
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

  Variables that uniquely identify margins: know repdem _deriv

. graph save AppendixFigure5, replace
(file AppendixFigure5.gph saved)

. 
. clear

. 
. 
. log close
      name:  <unnamed>
       log:  C:\Users\Dr. Badas\Desktop\Badas Simas PSRM Replication\BadasSimasPSRMreplication.log
  log type:  text
 closed on:  14 Mar 2021, 10:08:51
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